CS4287_Prj2_24273759_24293059_24284335.ipynb

CS4287_Prj2_24273759_24293059_24284335.ipynb (194675B)
      1 {
      2   "cells": [
      3     {
      4       "cell_type": "markdown",
      5       "metadata": {
      6         "id": "view-in-github",
      7         "colab_type": "text"
      8       },
      9       "source": [
     10         "<a href=\"https://colab.research.google.com/github/LindholmLabs/Neural-Computing/blob/main/CS4287_Prj2_24273759_24293059_24284335.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
     11       ]
     12     },
     13     {
     14       "cell_type": "markdown",
     15       "source": [
     16         "Lars Jacobs (24293059)\n",
     17         "William Lindholm (24273759)\n",
     18         "Patrick Vorreiter (24284335)\n"
     19       ],
     20       "metadata": {
     21         "id": "pC8VUn49vX5I"
     22       }
     23     },
     24     {
     25       "cell_type": "markdown",
     26       "source": [
     27         "The code executes until the end without error."
     28       ],
     29       "metadata": {
     30         "id": "B85XazoOvX5M"
     31       }
     32     },
     33     {
     34       "cell_type": "markdown",
     35       "source": [
     36         "References:\n",
     37         "\n",
     38         "  Inspiration taken from: Lecture P: RL – DQN and the Cartpole\n",
     39         "\n",
     40         "  understanding of Cartpole problem: https://www.gymlibrary.dev/environments/classic_control/cart_pole/"
     41       ],
     42       "metadata": {
     43         "id": "zHY7sojS4vsX"
     44       }
     45     },
     46     {
     47       "cell_type": "markdown",
     48       "source": [
     49         "# Assignment 3: Deep Reinforcement Learning"
     50       ],
     51       "metadata": {
     52         "id": "laWM2ZbBvX5N"
     53       }
     54     },
     55     {
     56       "cell_type": "markdown",
     57       "source": [
     58         "# 1. Why Reinforcement Learning is the machine learning paradigm of choice for this task\n",
     59         "\n",
     60         "Reinforcement Learning (RL) is useful for sequential decision-making problems where an agent learns to achieve a goal by interacting with an environment. Unlike supervised learning, RL doesn’t rely on labeled data but uses feedback in the form of rewards. In the context of the Mountain Car problem that we are looking at in this assignment, RL is particularly suitable because::\n",
     61         "-\tThe agent learns the optimal policy to move the car to the goal (the top of the mountain) by trial and error.\n",
     62         "-\tRL is effective in handling the continuous state space of the car’s position and velocity."
     63       ],
     64       "metadata": {
     65         "id": "wQPm2Et4vX5O"
     66       }
     67     },
     68     {
     69       "cell_type": "markdown",
     70       "source": [
     71         "# 2. The Gym Environment\n",
     72         "\n",
     73         "The Mountain Car environment is part of OpenAI Gym’s classic control suite with the following characteristics:\n",
     74         "- Goal: Drive the car up the hill on the right.\n",
     75         "- State Space: 2-dimensional vector (position, velocity).\n",
     76         "- Action Space: 3 discrete actions: accelerate to the left, don’t accelerate or accelerate to the right.\n",
     77         "- Reward: -1 per step until the goal is reached.\n"
     78       ],
     79       "metadata": {
     80         "id": "K_FW4DITvX5O"
     81       }
     82     },
     83     {
     84       "cell_type": "code",
     85       "execution_count": null,
     86       "metadata": {
     87         "colab": {
     88           "base_uri": "https://localhost:8080/"
     89         },
     90         "outputId": "50677207-bcbc-4721-b60b-0478eb1c9f1f",
     91         "id": "yxVXR0LOvX5O"
     92       },
     93       "outputs": [
     94         {
     95           "output_type": "stream",
     96           "name": "stderr",
     97           "text": [
     98             "/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/util.py:55: DeprecationWarning: jax.xla_computation is deprecated. Please use the AOT APIs; see https://jax.readthedocs.io/en/latest/aot.html. For example, replace xla_computation(f)(*xs) with jit(f).lower(*xs).compiler_ir('hlo'). See CHANGELOG.md for 0.4.30 for more examples.\n",
     99             "  from jax import xla_computation as _xla_computation\n",
    100             "/usr/local/lib/python3.10/dist-packages/gym/core.py:317: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
    101             "  deprecation(\n",
    102             "/usr/local/lib/python3.10/dist-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
    103             "  deprecation(\n"
    104           ]
    105         }
    106       ],
    107       "source": [
    108         "import gym\n",
    109         "import numpy as np\n",
    110         "import tensorflow as tf\n",
    111         "from collections import deque\n",
    112         "import matplotlib.pyplot as plt\n",
    113         "\n",
    114         "# Initialize the environment\n",
    115         "env = gym.make('MountainCar-v0')\n",
    116         "state_dim = env.observation_space.shape[0]\n",
    117         "action_dim = env.action_space.n"
    118       ]
    119     },
    120     {
    121       "cell_type": "markdown",
    122       "source": [
    123         "# 3. Implementation\n",
    124         "### a. Capture and sampling of the data"
    125       ],
    126       "metadata": {
    127         "id": "BUDPRxQ6vX5O"
    128       }
    129     },
    130     {
    131       "cell_type": "code",
    132       "source": [
    133         "# Replay buffer for up to 50 000 experiences\n",
    134         "replay_buffer = deque(maxlen=50000)"
    135       ],
    136       "metadata": {
    137         "id": "WMbshJ-96vE7"
    138       },
    139       "execution_count": null,
    140       "outputs": []
    141     },
    142     {
    143       "cell_type": "code",
    144       "source": [
    145         "def sample_experiences(batch_size):\n",
    146         "    # Randomly select a batch of experiences from the replay buffer\n",
    147         "    indices = np.random.choice(len(replay_buffer), batch_size)\n",
    148         "    batch = [replay_buffer[index] for index in indices]\n",
    149         "\n",
    150         "    # Unzip the batch into separate components\n",
    151         "    states, actions, rewards, next_states, dones = zip(*batch)\n",
    152         "    return (\n",
    153         "        np.array(states),\n",
    154         "        np.array(actions),\n",
    155         "        np.array(rewards),\n",
    156         "        np.array(next_states),\n",
    157         "        np.array(dones),\n",
    158         "    )"
    159       ],
    160       "metadata": {
    161         "id": "B5iJCGZFvX5O"
    162       },
    163       "execution_count": null,
    164       "outputs": []
    165     },
    166     {
    167       "cell_type": "markdown",
    168       "source": [
    169         "### b. Network Structure\n",
    170         "\n",
    171         "For the DQN we use a simple feed-forward neural network to approximate Q-values for each action."
    172       ],
    173       "metadata": {
    174         "id": "lxZB1apSvX5P"
    175       }
    176     },
    177     {
    178       "cell_type": "code",
    179       "source": [
    180         "# Define the DQN model\n",
    181         "model = tf.keras.Sequential([\n",
    182         "    tf.keras.layers.Dense(128, activation=\"relu\", input_shape=(state_dim,)),\n",
    183         "    tf.keras.layers.Dense(128, activation=\"relu\"),\n",
    184         "    tf.keras.layers.Dense(action_dim)\n",
    185         "])"
    186       ],
    187       "metadata": {
    188         "colab": {
    189           "base_uri": "https://localhost:8080/"
    190         },
    191         "outputId": "0efe95a7-4ef4-409b-a284-892f3f485c67",
    192         "id": "Zul0NK8svX5P"
    193       },
    194       "execution_count": null,
    195       "outputs": [
    196         {
    197           "output_type": "stream",
    198           "name": "stderr",
    199           "text": [
    200             "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
    201             "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
    202           ]
    203         }
    204       ]
    205     },
    206     {
    207       "cell_type": "code",
    208       "source": [
    209         "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n",
    210         "loss_fn = tf.keras.losses.MeanSquaredError()"
    211       ],
    212       "metadata": {
    213         "id": "Isf4udiz5Vzw"
    214       },
    215       "execution_count": null,
    216       "outputs": []
    217     },
    218     {
    219       "cell_type": "markdown",
    220       "source": [
    221         "### c. Q-Learning Update"
    222       ],
    223       "metadata": {
    224         "id": "OFcaK5T5vX5P"
    225       }
    226     },
    227     {
    228       "cell_type": "markdown",
    229       "source": [
    230         "In Q-learning, the goal is to update the Q-values, which represent the expected cumulative rewards for taking an action in a given state. The Q-values are updated using the Bellman equation:\n",
    231         "\n",
    232         "$$\n",
    233         "Q_{target} = r + \\gamma \\max_a Q(s', a)\n",
    234         "$$\n",
    235         "\n",
    236         "Source: Lecture P: RL – DQN and the Cartpole slide 9\n",
    237         "\n",
    238         "Where $r$ is the immediate reward, $\\gamma$ is the discount factor, and $\\max_a Q(s', a)$ is the maximum Q-value for the next state.\n",
    239         "\n",
    240         "First, the model predicts the Q-values for the current state. The target Q-values are computed using the Bellman equation, where we add the immediate reward and the discounted maximum future Q-value from the next state. The loss is the difference between the predicted Q-values and the target Q-values, that we are calculating using the Mean Squared Error. This measures how far off the predictions are from the targets. The models weights are updated by minimizing this loss using gradient descent, which allows the agent to adjust its policy and improve over time."
    241       ],
    242       "metadata": {
    243         "id": "SwVr5QOkF5he"
    244       }
    245     },
    246     {
    247       "cell_type": "code",
    248       "source": [
    249         "discount_factor = 0.95"
    250       ],
    251       "metadata": {
    252         "id": "9F1u69bw5hpS"
    253       },
    254       "execution_count": null,
    255       "outputs": []
    256     },
    257     {
    258       "cell_type": "code",
    259       "source": [
    260         "def training_step(batch_size):\n",
    261         "    # Sample a batch of experiences from the replay buffer\n",
    262         "    states, actions, rewards, next_states, dones = sample_experiences(batch_size)\n",
    263         "\n",
    264         "    # Get Q-values for the next states and compute the maximum Q-values\n",
    265         "    next_Q_values = model.predict(next_states, verbose=0)\n",
    266         "    max_next_Q_values = np.max(next_Q_values, axis=1)\n",
    267         "\n",
    268         "    # Calculate the target Q-values using the Bellman equation (explained in 3.C)\n",
    269         "    target_Q_values = rewards + (1 - dones) * discount_factor * max_next_Q_values\n",
    270         "    target_Q_values = target_Q_values.reshape(-1, 1)\n",
    271         "\n",
    272         "    # Create a mask for the selected actions\n",
    273         "    mask = tf.one_hot(actions, action_dim)\n",
    274         "\n",
    275         "    # Compute gradients and apply updates to the model\n",
    276         "    with tf.GradientTape() as tape:\n",
    277         "        all_Q_values = model(states)\n",
    278         "        Q_values = tf.reduce_sum(all_Q_values * mask, axis=1, keepdims=True)\n",
    279         "        loss = loss_fn(target_Q_values, Q_values)\n",
    280         "\n",
    281         "    grads = tape.gradient(loss, model.trainable_variables)\n",
    282         "    optimizer.apply_gradients(zip(grads, model.trainable_variables))"
    283       ],
    284       "metadata": {
    285         "id": "UDg8rxyWvX5P"
    286       },
    287       "execution_count": null,
    288       "outputs": []
    289     },
    290     {
    291       "cell_type": "code",
    292       "source": [
    293         "# Epsilon-greedy policy\n",
    294         "def epsilon_greedy_policy(state, epsilon):\n",
    295         "    # Explore: take a random action with probability epsilon\n",
    296         "    if np.random.rand() < epsilon:\n",
    297         "        return np.random.randint(action_dim)\n",
    298         "    # Exploit: choose the best action based on the model’s Q-values\n",
    299         "    else:\n",
    300         "      Q_values = model.predict(state[np.newaxis], verbose=0)\n",
    301         "      return np.argmax(Q_values[0])"
    302       ],
    303       "metadata": {
    304         "id": "vsfpJoWSvX5P"
    305       },
    306       "execution_count": null,
    307       "outputs": []
    308     },
    309     {
    310       "cell_type": "markdown",
    311       "source": [
    312         "### d. Advanced concepts\n"
    313       ],
    314       "metadata": {
    315         "id": "QApYeIjbvX5P"
    316       }
    317     },
    318     {
    319       "cell_type": "markdown",
    320       "source": [
    321         "**Random Seed Initialization**\n",
    322         "\n",
    323         "Random seed initialization ensures that the results of a model can be reproduced. By setting a fixed seed for NumPy and TensorFlow, we can ensure that random processes will always produce the same results.\n",
    324         "\n",
    325         "**Catastrophic Forgetting**\n",
    326         "\n",
    327         "One of the main hurdles we had to overcome was that our RL agent suffered from catastrophic forgetting during training, it would get a good result for a number of episodes, revert back to a poor policy, and keep doing that for hundreds of episodes, see graph below. Sometimes it would take it almost 100 episodes to regain the lost performance. See the sudden drop just after episode 50 in the graph below. We managed to mitigate this problem by decreasing the learning rate significantly, after noticing it was too high, which led to wild \"mood swings\" in the agent. We also tried implementing a dropout, since we hypothesised that it could help the neural network rely less on specific neurons thus improving performance, but we did not find any positive impact from this.\n",
    328         "\n",
    329         "**Discount factor**\n",
    330         "\n",
    331         "We chose a relatively high discount factor of 0.95, since we wanted our RL agent to not be short sighted.\n",
    332         "\n",
    333         "**learning rate schedule**\n",
    334         "\n",
    335         "We chose to implement a learning rate schedule as it is something which we noticed improved performance in our previous project, and we think having a \"wild\" exploratory phase would be beneficial for adjusting quickly in the beginning, when all the weights are random and untrained.\n",
    336         "\n",
    337         "**epsilon decay**\n",
    338         "In the beginning we wanted the agent to explore as much as possible, so that it could stumle upon moves which would land it a better reward. But after finding such a scenario, we also wanted it to remember it, so that it does not forget its training, which we had quite a lot of issues with in the beginning. Therefore we decided on a relatively high epsilon value of 0.1, which allows the agent to explore. Then we gradually lower it over the next 225 episodes down to 0.010. Allowing it to instead exploit.\n",
    339         "\n"
    340       ],
    341       "metadata": {
    342         "id": "eX842iCZNuo9"
    343       }
    344     },
    345     {
    346       "cell_type": "markdown",
    347       "source": [
    348         "![WhatsApp Bild 2024-12-05 kl. 15.43.12_f190bb80.jpg](data:image/jpeg;base64,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)"
    349       ],
    350       "metadata": {
    351         "id": "WCqtY2Fqzcec"
    352       }
    353     },
    354     {
    355       "cell_type": "code",
    356       "source": [
    357         "np.random.seed(42)\n",
    358         "tf.random.set_seed(42)"
    359       ],
    360       "metadata": {
    361         "id": "VmZDH5s95kmk"
    362       },
    363       "execution_count": null,
    364       "outputs": []
    365     },
    366     {
    367       "cell_type": "markdown",
    368       "source": [
    369         "# 4. Results"
    370       ],
    371       "metadata": {
    372         "id": "gi6NrA4xvX5P"
    373       }
    374     },
    375     {
    376       "cell_type": "code",
    377       "source": [
    378         "episodes = 300\n",
    379         "max_steps = 200\n",
    380         "epsilon = 0.1\n",
    381         "epsilon_decay = 0.990\n",
    382         "epsilon_min = 0.01\n",
    383         "rewards_history = []\n",
    384         "best_score = 0\n",
    385         "best_weights = None"
    386       ],
    387       "metadata": {
    388         "id": "3RTBTfRY5nzC"
    389       },
    390       "execution_count": null,
    391       "outputs": []
    392     },
    393     {
    394       "cell_type": "code",
    395       "source": [
    396         "# Training Loop\n",
    397         "for episode in range(episodes):\n",
    398         "    # Reset environment at the start of each episode\n",
    399         "    state = env.reset()\n",
    400         "    # Initialize total reward for the episode\n",
    401         "    total_reward = 0\n",
    402         "\n",
    403         "    for step in range(max_steps):\n",
    404         "        # Choose action using epsilon-greedy policy\n",
    405         "        action = epsilon_greedy_policy(state, epsilon)\n",
    406         "        next_state, reward, done, _ = env.step(action)\n",
    407         "\n",
    408         "        # Store the experience in the replay buffer\n",
    409         "        replay_buffer.append((state, action, reward, next_state, done))\n",
    410         "        state = next_state\n",
    411         "        total_reward += reward\n",
    412         "\n",
    413         "        # Exit loop if the episode is finished\n",
    414         "        if done:\n",
    415         "            break\n",
    416         "\n",
    417         "        # Perform one training step\n",
    418         "        training_step(batch_size=32)\n",
    419         "\n",
    420         "    # Check if this episode's reward is better than the previous best\n",
    421         "    if total_reward > best_score:\n",
    422         "        best_weights = model.get_weights()\n",
    423         "        best_score = total_reward\n",
    424         "\n",
    425         "    # Adjust learning rate based on progress\n",
    426         "    if episode == 100:  # lower learning rate after 100 episodes\n",
    427         "        optimizer = tf.keras.optimizers.Adam(learning_rate=0.005)\n",
    428         "    elif episode == 200:  # lower it again after 200 episodes\n",
    429         "        optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
    430         "\n",
    431         "    print(f\"Episode {episode + 1}/{episodes}, Steps: {step + 1}, Reward: {total_reward}, Epsilon: {epsilon:.3f}\")\n",
    432         "\n",
    433         "    # Decay epsilon to reduce exploration over time\n",
    434         "    epsilon = max(epsilon * epsilon_decay, epsilon_min)\n",
    435         "\n",
    436         "    # Store total reward for the episode\n",
    437         "    rewards_history.append(total_reward)\n",
    438         "\n",
    439         "# After all episodes, set the model to the best weights\n",
    440         "if best_weights is not None:\n",
    441         "    model.set_weights(best_weights)"
    442       ],
    443       "metadata": {
    444         "colab": {
    445           "base_uri": "https://localhost:8080/"
    446         },
    447         "outputId": "c943f300-c270-4518-bd66-ecfa2a728334",
    448         "id": "Sr4hL-Q4vX5P"
    449       },
    450       "execution_count": null,
    451       "outputs": [
    452         {
    453           "output_type": "stream",
    454           "name": "stderr",
    455           "text": [
    456             "/usr/local/lib/python3.10/dist-packages/gym/utils/passive_env_checker.py:241: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
    457             "  if not isinstance(terminated, (bool, np.bool8)):\n"
    458           ]
    459         },
    460         {
    461           "output_type": "stream",
    462           "name": "stdout",
    463           "text": [
    464             "Episode 1/300, Steps: 200, Reward: -200.0, Epsilon: 0.100\n",
    465             "Episode 2/300, Steps: 200, Reward: -200.0, Epsilon: 0.099\n",
    466             "Episode 3/300, Steps: 200, Reward: -200.0, Epsilon: 0.098\n",
    467             "Episode 4/300, Steps: 200, Reward: -200.0, Epsilon: 0.097\n",
    468             "Episode 5/300, Steps: 200, Reward: -200.0, Epsilon: 0.096\n",
    469             "Episode 6/300, Steps: 200, Reward: -200.0, Epsilon: 0.095\n",
    470             "Episode 7/300, Steps: 200, Reward: -200.0, Epsilon: 0.094\n",
    471             "Episode 8/300, Steps: 200, Reward: -200.0, Epsilon: 0.093\n",
    472             "Episode 9/300, Steps: 200, Reward: -200.0, Epsilon: 0.092\n",
    473             "Episode 10/300, Steps: 200, Reward: -200.0, Epsilon: 0.091\n",
    474             "Episode 11/300, Steps: 200, Reward: -200.0, Epsilon: 0.090\n",
    475             "Episode 12/300, Steps: 200, Reward: -200.0, Epsilon: 0.090\n",
    476             "Episode 13/300, Steps: 200, Reward: -200.0, Epsilon: 0.089\n",
    477             "Episode 14/300, Steps: 200, Reward: -200.0, Epsilon: 0.088\n",
    478             "Episode 15/300, Steps: 200, Reward: -200.0, Epsilon: 0.087\n",
    479             "Episode 16/300, Steps: 200, Reward: -200.0, Epsilon: 0.086\n",
    480             "Episode 17/300, Steps: 200, Reward: -200.0, Epsilon: 0.085\n",
    481             "Episode 18/300, Steps: 200, Reward: -200.0, Epsilon: 0.084\n",
    482             "Episode 19/300, Steps: 200, Reward: -200.0, Epsilon: 0.083\n",
    483             "Episode 20/300, Steps: 200, Reward: -200.0, Epsilon: 0.083\n",
    484             "Episode 21/300, Steps: 200, Reward: -200.0, Epsilon: 0.082\n",
    485             "Episode 22/300, Steps: 200, Reward: -200.0, Epsilon: 0.081\n",
    486             "Episode 23/300, Steps: 200, Reward: -200.0, Epsilon: 0.080\n",
    487             "Episode 24/300, Steps: 200, Reward: -200.0, Epsilon: 0.079\n",
    488             "Episode 25/300, Steps: 200, Reward: -200.0, Epsilon: 0.079\n",
    489             "Episode 26/300, Steps: 200, Reward: -200.0, Epsilon: 0.078\n",
    490             "Episode 27/300, Steps: 200, Reward: -200.0, Epsilon: 0.077\n",
    491             "Episode 28/300, Steps: 200, Reward: -200.0, Epsilon: 0.076\n",
    492             "Episode 29/300, Steps: 200, Reward: -200.0, Epsilon: 0.075\n",
    493             "Episode 30/300, Steps: 200, Reward: -200.0, Epsilon: 0.075\n",
    494             "Episode 31/300, Steps: 200, Reward: -200.0, Epsilon: 0.074\n",
    495             "Episode 32/300, Steps: 200, Reward: -200.0, Epsilon: 0.073\n",
    496             "Episode 33/300, Steps: 200, Reward: -200.0, Epsilon: 0.072\n",
    497             "Episode 34/300, Steps: 200, Reward: -200.0, Epsilon: 0.072\n",
    498             "Episode 35/300, Steps: 200, Reward: -200.0, Epsilon: 0.071\n",
    499             "Episode 36/300, Steps: 200, Reward: -200.0, Epsilon: 0.070\n",
    500             "Episode 37/300, Steps: 200, Reward: -200.0, Epsilon: 0.070\n",
    501             "Episode 38/300, Steps: 200, Reward: -200.0, Epsilon: 0.069\n",
    502             "Episode 39/300, Steps: 200, Reward: -200.0, Epsilon: 0.068\n",
    503             "Episode 40/300, Steps: 200, Reward: -200.0, Epsilon: 0.068\n",
    504             "Episode 41/300, Steps: 200, Reward: -200.0, Epsilon: 0.067\n",
    505             "Episode 42/300, Steps: 200, Reward: -200.0, Epsilon: 0.066\n",
    506             "Episode 43/300, Steps: 200, Reward: -200.0, Epsilon: 0.066\n",
    507             "Episode 44/300, Steps: 200, Reward: -200.0, Epsilon: 0.065\n",
    508             "Episode 45/300, Steps: 200, Reward: -200.0, Epsilon: 0.064\n",
    509             "Episode 46/300, Steps: 200, Reward: -200.0, Epsilon: 0.064\n",
    510             "Episode 47/300, Steps: 200, Reward: -200.0, Epsilon: 0.063\n",
    511             "Episode 48/300, Steps: 200, Reward: -200.0, Epsilon: 0.062\n",
    512             "Episode 49/300, Steps: 200, Reward: -200.0, Epsilon: 0.062\n",
    513             "Episode 50/300, Steps: 200, Reward: -200.0, Epsilon: 0.061\n",
    514             "Episode 51/300, Steps: 200, Reward: -200.0, Epsilon: 0.061\n",
    515             "Episode 52/300, Steps: 200, Reward: -200.0, Epsilon: 0.060\n",
    516             "Episode 53/300, Steps: 200, Reward: -200.0, Epsilon: 0.059\n",
    517             "Episode 54/300, Steps: 200, Reward: -200.0, Epsilon: 0.059\n",
    518             "Episode 55/300, Steps: 200, Reward: -200.0, Epsilon: 0.058\n",
    519             "Episode 56/300, Steps: 200, Reward: -200.0, Epsilon: 0.058\n",
    520             "Episode 57/300, Steps: 200, Reward: -200.0, Epsilon: 0.057\n",
    521             "Episode 58/300, Steps: 200, Reward: -200.0, Epsilon: 0.056\n",
    522             "Episode 59/300, Steps: 200, Reward: -200.0, Epsilon: 0.056\n",
    523             "Episode 60/300, Steps: 200, Reward: -200.0, Epsilon: 0.055\n",
    524             "Episode 61/300, Steps: 200, Reward: -200.0, Epsilon: 0.055\n",
    525             "Episode 62/300, Steps: 200, Reward: -200.0, Epsilon: 0.054\n",
    526             "Episode 63/300, Steps: 200, Reward: -200.0, Epsilon: 0.054\n",
    527             "Episode 64/300, Steps: 200, Reward: -200.0, Epsilon: 0.053\n",
    528             "Episode 65/300, Steps: 200, Reward: -200.0, Epsilon: 0.053\n",
    529             "Episode 66/300, Steps: 200, Reward: -200.0, Epsilon: 0.052\n",
    530             "Episode 67/300, Steps: 200, Reward: -200.0, Epsilon: 0.052\n",
    531             "Episode 68/300, Steps: 200, Reward: -200.0, Epsilon: 0.051\n",
    532             "Episode 69/300, Steps: 200, Reward: -200.0, Epsilon: 0.050\n",
    533             "Episode 70/300, Steps: 200, Reward: -200.0, Epsilon: 0.050\n",
    534             "Episode 71/300, Steps: 200, Reward: -200.0, Epsilon: 0.049\n",
    535             "Episode 72/300, Steps: 200, Reward: -200.0, Epsilon: 0.049\n",
    536             "Episode 73/300, Steps: 200, Reward: -200.0, Epsilon: 0.048\n",
    537             "Episode 74/300, Steps: 200, Reward: -200.0, Epsilon: 0.048\n",
    538             "Episode 75/300, Steps: 200, Reward: -200.0, Epsilon: 0.048\n",
    539             "Episode 76/300, Steps: 200, Reward: -200.0, Epsilon: 0.047\n",
    540             "Episode 77/300, Steps: 200, Reward: -200.0, Epsilon: 0.047\n",
    541             "Episode 78/300, Steps: 200, Reward: -200.0, Epsilon: 0.046\n",
    542             "Episode 79/300, Steps: 200, Reward: -200.0, Epsilon: 0.046\n",
    543             "Episode 80/300, Steps: 200, Reward: -200.0, Epsilon: 0.045\n",
    544             "Episode 81/300, Steps: 200, Reward: -200.0, Epsilon: 0.045\n",
    545             "Episode 82/300, Steps: 200, Reward: -200.0, Epsilon: 0.044\n",
    546             "Episode 83/300, Steps: 200, Reward: -200.0, Epsilon: 0.044\n",
    547             "Episode 84/300, Steps: 200, Reward: -200.0, Epsilon: 0.043\n",
    548             "Episode 85/300, Steps: 200, Reward: -200.0, Epsilon: 0.043\n",
    549             "Episode 86/300, Steps: 200, Reward: -200.0, Epsilon: 0.043\n",
    550             "Episode 87/300, Steps: 200, Reward: -200.0, Epsilon: 0.042\n",
    551             "Episode 88/300, Steps: 200, Reward: -200.0, Epsilon: 0.042\n",
    552             "Episode 89/300, Steps: 200, Reward: -200.0, Epsilon: 0.041\n",
    553             "Episode 90/300, Steps: 200, Reward: -200.0, Epsilon: 0.041\n",
    554             "Episode 91/300, Steps: 200, Reward: -200.0, Epsilon: 0.040\n",
    555             "Episode 92/300, Steps: 200, Reward: -200.0, Epsilon: 0.040\n",
    556             "Episode 93/300, Steps: 200, Reward: -200.0, Epsilon: 0.040\n",
    557             "Episode 94/300, Steps: 200, Reward: -200.0, Epsilon: 0.039\n",
    558             "Episode 95/300, Steps: 200, Reward: -200.0, Epsilon: 0.039\n",
    559             "Episode 96/300, Steps: 200, Reward: -200.0, Epsilon: 0.038\n",
    560             "Episode 97/300, Steps: 200, Reward: -200.0, Epsilon: 0.038\n",
    561             "Episode 98/300, Steps: 200, Reward: -200.0, Epsilon: 0.038\n",
    562             "Episode 99/300, Steps: 200, Reward: -200.0, Epsilon: 0.037\n",
    563             "Episode 100/300, Steps: 200, Reward: -200.0, Epsilon: 0.037\n",
    564             "Episode 101/300, Steps: 200, Reward: -200.0, Epsilon: 0.037\n",
    565             "Episode 102/300, Steps: 200, Reward: -200.0, Epsilon: 0.036\n",
    566             "Episode 103/300, Steps: 200, Reward: -200.0, Epsilon: 0.036\n",
    567             "Episode 104/300, Steps: 200, Reward: -200.0, Epsilon: 0.036\n",
    568             "Episode 105/300, Steps: 200, Reward: -200.0, Epsilon: 0.035\n",
    569             "Episode 106/300, Steps: 200, Reward: -200.0, Epsilon: 0.035\n",
    570             "Episode 107/300, Steps: 200, Reward: -200.0, Epsilon: 0.034\n",
    571             "Episode 108/300, Steps: 200, Reward: -200.0, Epsilon: 0.034\n",
    572             "Episode 109/300, Steps: 151, Reward: -151.0, Epsilon: 0.034\n",
    573             "Episode 110/300, Steps: 200, Reward: -200.0, Epsilon: 0.033\n",
    574             "Episode 111/300, Steps: 200, Reward: -200.0, Epsilon: 0.033\n",
    575             "Episode 112/300, Steps: 200, Reward: -200.0, Epsilon: 0.033\n",
    576             "Episode 113/300, Steps: 200, Reward: -200.0, Epsilon: 0.032\n",
    577             "Episode 114/300, Steps: 200, Reward: -200.0, Epsilon: 0.032\n",
    578             "Episode 115/300, Steps: 200, Reward: -200.0, Epsilon: 0.032\n",
    579             "Episode 116/300, Steps: 200, Reward: -200.0, Epsilon: 0.031\n",
    580             "Episode 117/300, Steps: 200, Reward: -200.0, Epsilon: 0.031\n",
    581             "Episode 118/300, Steps: 200, Reward: -200.0, Epsilon: 0.031\n",
    582             "Episode 119/300, Steps: 200, Reward: -200.0, Epsilon: 0.031\n",
    583             "Episode 120/300, Steps: 200, Reward: -200.0, Epsilon: 0.030\n",
    584             "Episode 121/300, Steps: 200, Reward: -200.0, Epsilon: 0.030\n",
    585             "Episode 122/300, Steps: 200, Reward: -200.0, Epsilon: 0.030\n",
    586             "Episode 123/300, Steps: 186, Reward: -186.0, Epsilon: 0.029\n",
    587             "Episode 124/300, Steps: 200, Reward: -200.0, Epsilon: 0.029\n",
    588             "Episode 125/300, Steps: 200, Reward: -200.0, Epsilon: 0.029\n",
    589             "Episode 126/300, Steps: 147, Reward: -147.0, Epsilon: 0.028\n",
    590             "Episode 127/300, Steps: 143, Reward: -143.0, Epsilon: 0.028\n",
    591             "Episode 128/300, Steps: 200, Reward: -200.0, Epsilon: 0.028\n",
    592             "Episode 129/300, Steps: 200, Reward: -200.0, Epsilon: 0.028\n",
    593             "Episode 130/300, Steps: 200, Reward: -200.0, Epsilon: 0.027\n",
    594             "Episode 131/300, Steps: 200, Reward: -200.0, Epsilon: 0.027\n",
    595             "Episode 132/300, Steps: 200, Reward: -200.0, Epsilon: 0.027\n",
    596             "Episode 133/300, Steps: 200, Reward: -200.0, Epsilon: 0.027\n",
    597             "Episode 134/300, Steps: 200, Reward: -200.0, Epsilon: 0.026\n",
    598             "Episode 135/300, Steps: 200, Reward: -200.0, Epsilon: 0.026\n",
    599             "Episode 136/300, Steps: 200, Reward: -200.0, Epsilon: 0.026\n",
    600             "Episode 137/300, Steps: 200, Reward: -200.0, Epsilon: 0.025\n",
    601             "Episode 138/300, Steps: 200, Reward: -200.0, Epsilon: 0.025\n",
    602             "Episode 139/300, Steps: 200, Reward: -200.0, Epsilon: 0.025\n",
    603             "Episode 140/300, Steps: 128, Reward: -128.0, Epsilon: 0.025\n",
    604             "Episode 141/300, Steps: 200, Reward: -200.0, Epsilon: 0.024\n",
    605             "Episode 142/300, Steps: 200, Reward: -200.0, Epsilon: 0.024\n",
    606             "Episode 143/300, Steps: 200, Reward: -200.0, Epsilon: 0.024\n",
    607             "Episode 144/300, Steps: 200, Reward: -200.0, Epsilon: 0.024\n",
    608             "Episode 145/300, Steps: 200, Reward: -200.0, Epsilon: 0.024\n",
    609             "Episode 146/300, Steps: 200, Reward: -200.0, Epsilon: 0.023\n",
    610             "Episode 147/300, Steps: 200, Reward: -200.0, Epsilon: 0.023\n",
    611             "Episode 148/300, Steps: 200, Reward: -200.0, Epsilon: 0.023\n",
    612             "Episode 149/300, Steps: 200, Reward: -200.0, Epsilon: 0.023\n",
    613             "Episode 150/300, Steps: 200, Reward: -200.0, Epsilon: 0.022\n",
    614             "Episode 151/300, Steps: 200, Reward: -200.0, Epsilon: 0.022\n",
    615             "Episode 152/300, Steps: 200, Reward: -200.0, Epsilon: 0.022\n",
    616             "Episode 153/300, Steps: 192, Reward: -192.0, Epsilon: 0.022\n",
    617             "Episode 154/300, Steps: 200, Reward: -200.0, Epsilon: 0.021\n",
    618             "Episode 155/300, Steps: 175, Reward: -175.0, Epsilon: 0.021\n",
    619             "Episode 156/300, Steps: 187, Reward: -187.0, Epsilon: 0.021\n",
    620             "Episode 157/300, Steps: 200, Reward: -200.0, Epsilon: 0.021\n",
    621             "Episode 158/300, Steps: 200, Reward: -200.0, Epsilon: 0.021\n",
    622             "Episode 159/300, Steps: 118, Reward: -118.0, Epsilon: 0.020\n",
    623             "Episode 160/300, Steps: 200, Reward: -200.0, Epsilon: 0.020\n",
    624             "Episode 161/300, Steps: 200, Reward: -200.0, Epsilon: 0.020\n",
    625             "Episode 162/300, Steps: 106, Reward: -106.0, Epsilon: 0.020\n",
    626             "Episode 163/300, Steps: 200, Reward: -200.0, Epsilon: 0.020\n",
    627             "Episode 164/300, Steps: 200, Reward: -200.0, Epsilon: 0.019\n",
    628             "Episode 165/300, Steps: 125, Reward: -125.0, Epsilon: 0.019\n",
    629             "Episode 166/300, Steps: 200, Reward: -200.0, Epsilon: 0.019\n",
    630             "Episode 167/300, Steps: 200, Reward: -200.0, Epsilon: 0.019\n",
    631             "Episode 168/300, Steps: 200, Reward: -200.0, Epsilon: 0.019\n",
    632             "Episode 169/300, Steps: 157, Reward: -157.0, Epsilon: 0.018\n",
    633             "Episode 170/300, Steps: 200, Reward: -200.0, Epsilon: 0.018\n",
    634             "Episode 171/300, Steps: 116, Reward: -116.0, Epsilon: 0.018\n",
    635             "Episode 172/300, Steps: 175, Reward: -175.0, Epsilon: 0.018\n",
    636             "Episode 173/300, Steps: 200, Reward: -200.0, Epsilon: 0.018\n",
    637             "Episode 174/300, Steps: 178, Reward: -178.0, Epsilon: 0.018\n",
    638             "Episode 175/300, Steps: 200, Reward: -200.0, Epsilon: 0.017\n",
    639             "Episode 176/300, Steps: 154, Reward: -154.0, Epsilon: 0.017\n",
    640             "Episode 177/300, Steps: 185, Reward: -185.0, Epsilon: 0.017\n",
    641             "Episode 178/300, Steps: 129, Reward: -129.0, Epsilon: 0.017\n",
    642             "Episode 179/300, Steps: 200, Reward: -200.0, Epsilon: 0.017\n",
    643             "Episode 180/300, Steps: 200, Reward: -200.0, Epsilon: 0.017\n",
    644             "Episode 181/300, Steps: 164, Reward: -164.0, Epsilon: 0.016\n",
    645             "Episode 182/300, Steps: 200, Reward: -200.0, Epsilon: 0.016\n",
    646             "Episode 183/300, Steps: 200, Reward: -200.0, Epsilon: 0.016\n",
    647             "Episode 184/300, Steps: 149, Reward: -149.0, Epsilon: 0.016\n",
    648             "Episode 185/300, Steps: 178, Reward: -178.0, Epsilon: 0.016\n",
    649             "Episode 186/300, Steps: 190, Reward: -190.0, Epsilon: 0.016\n",
    650             "Episode 187/300, Steps: 200, Reward: -200.0, Epsilon: 0.015\n",
    651             "Episode 188/300, Steps: 149, Reward: -149.0, Epsilon: 0.015\n",
    652             "Episode 189/300, Steps: 169, Reward: -169.0, Epsilon: 0.015\n",
    653             "Episode 190/300, Steps: 142, Reward: -142.0, Epsilon: 0.015\n",
    654             "Episode 191/300, Steps: 200, Reward: -200.0, Epsilon: 0.015\n",
    655             "Episode 192/300, Steps: 200, Reward: -200.0, Epsilon: 0.015\n",
    656             "Episode 193/300, Steps: 200, Reward: -200.0, Epsilon: 0.015\n",
    657             "Episode 194/300, Steps: 104, Reward: -104.0, Epsilon: 0.014\n",
    658             "Episode 195/300, Steps: 200, Reward: -200.0, Epsilon: 0.014\n",
    659             "Episode 196/300, Steps: 95, Reward: -95.0, Epsilon: 0.014\n",
    660             "Episode 197/300, Steps: 200, Reward: -200.0, Epsilon: 0.014\n",
    661             "Episode 198/300, Steps: 148, Reward: -148.0, Epsilon: 0.014\n",
    662             "Episode 199/300, Steps: 143, Reward: -143.0, Epsilon: 0.014\n",
    663             "Episode 200/300, Steps: 200, Reward: -200.0, Epsilon: 0.014\n",
    664             "Episode 201/300, Steps: 200, Reward: -200.0, Epsilon: 0.013\n",
    665             "Episode 202/300, Steps: 107, Reward: -107.0, Epsilon: 0.013\n",
    666             "Episode 203/300, Steps: 197, Reward: -197.0, Epsilon: 0.013\n",
    667             "Episode 204/300, Steps: 200, Reward: -200.0, Epsilon: 0.013\n",
    668             "Episode 205/300, Steps: 200, Reward: -200.0, Epsilon: 0.013\n",
    669             "Episode 206/300, Steps: 200, Reward: -200.0, Epsilon: 0.013\n",
    670             "Episode 207/300, Steps: 138, Reward: -138.0, Epsilon: 0.013\n",
    671             "Episode 208/300, Steps: 200, Reward: -200.0, Epsilon: 0.012\n",
    672             "Episode 209/300, Steps: 200, Reward: -200.0, Epsilon: 0.012\n",
    673             "Episode 210/300, Steps: 200, Reward: -200.0, Epsilon: 0.012\n",
    674             "Episode 211/300, Steps: 161, Reward: -161.0, Epsilon: 0.012\n",
    675             "Episode 212/300, Steps: 137, Reward: -137.0, Epsilon: 0.012\n",
    676             "Episode 213/300, Steps: 194, Reward: -194.0, Epsilon: 0.012\n",
    677             "Episode 214/300, Steps: 200, Reward: -200.0, Epsilon: 0.012\n",
    678             "Episode 215/300, Steps: 200, Reward: -200.0, Epsilon: 0.012\n",
    679             "Episode 216/300, Steps: 200, Reward: -200.0, Epsilon: 0.012\n",
    680             "Episode 217/300, Steps: 200, Reward: -200.0, Epsilon: 0.011\n",
    681             "Episode 218/300, Steps: 182, Reward: -182.0, Epsilon: 0.011\n",
    682             "Episode 219/300, Steps: 200, Reward: -200.0, Epsilon: 0.011\n",
    683             "Episode 220/300, Steps: 168, Reward: -168.0, Epsilon: 0.011\n",
    684             "Episode 221/300, Steps: 200, Reward: -200.0, Epsilon: 0.011\n",
    685             "Episode 222/300, Steps: 142, Reward: -142.0, Epsilon: 0.011\n",
    686             "Episode 223/300, Steps: 200, Reward: -200.0, Epsilon: 0.011\n",
    687             "Episode 224/300, Steps: 200, Reward: -200.0, Epsilon: 0.011\n",
    688             "Episode 225/300, Steps: 200, Reward: -200.0, Epsilon: 0.011\n",
    689             "Episode 226/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    690             "Episode 227/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    691             "Episode 228/300, Steps: 173, Reward: -173.0, Epsilon: 0.010\n",
    692             "Episode 229/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    693             "Episode 230/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    694             "Episode 231/300, Steps: 131, Reward: -131.0, Epsilon: 0.010\n",
    695             "Episode 232/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    696             "Episode 233/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    697             "Episode 234/300, Steps: 181, Reward: -181.0, Epsilon: 0.010\n",
    698             "Episode 235/300, Steps: 120, Reward: -120.0, Epsilon: 0.010\n",
    699             "Episode 236/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    700             "Episode 237/300, Steps: 151, Reward: -151.0, Epsilon: 0.010\n",
    701             "Episode 238/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    702             "Episode 239/300, Steps: 172, Reward: -172.0, Epsilon: 0.010\n",
    703             "Episode 240/300, Steps: 156, Reward: -156.0, Epsilon: 0.010\n",
    704             "Episode 241/300, Steps: 112, Reward: -112.0, Epsilon: 0.010\n",
    705             "Episode 242/300, Steps: 180, Reward: -180.0, Epsilon: 0.010\n",
    706             "Episode 243/300, Steps: 175, Reward: -175.0, Epsilon: 0.010\n",
    707             "Episode 244/300, Steps: 195, Reward: -195.0, Epsilon: 0.010\n",
    708             "Episode 245/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    709             "Episode 246/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    710             "Episode 247/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    711             "Episode 248/300, Steps: 117, Reward: -117.0, Epsilon: 0.010\n",
    712             "Episode 249/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    713             "Episode 250/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    714             "Episode 251/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    715             "Episode 252/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    716             "Episode 253/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    717             "Episode 254/300, Steps: 163, Reward: -163.0, Epsilon: 0.010\n",
    718             "Episode 255/300, Steps: 145, Reward: -145.0, Epsilon: 0.010\n",
    719             "Episode 256/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    720             "Episode 257/300, Steps: 141, Reward: -141.0, Epsilon: 0.010\n",
    721             "Episode 258/300, Steps: 160, Reward: -160.0, Epsilon: 0.010\n",
    722             "Episode 259/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    723             "Episode 260/300, Steps: 107, Reward: -107.0, Epsilon: 0.010\n",
    724             "Episode 261/300, Steps: 175, Reward: -175.0, Epsilon: 0.010\n",
    725             "Episode 262/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    726             "Episode 263/300, Steps: 171, Reward: -171.0, Epsilon: 0.010\n",
    727             "Episode 264/300, Steps: 180, Reward: -180.0, Epsilon: 0.010\n",
    728             "Episode 265/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    729             "Episode 266/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    730             "Episode 267/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    731             "Episode 268/300, Steps: 121, Reward: -121.0, Epsilon: 0.010\n",
    732             "Episode 269/300, Steps: 191, Reward: -191.0, Epsilon: 0.010\n",
    733             "Episode 270/300, Steps: 153, Reward: -153.0, Epsilon: 0.010\n",
    734             "Episode 271/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    735             "Episode 272/300, Steps: 190, Reward: -190.0, Epsilon: 0.010\n",
    736             "Episode 273/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    737             "Episode 274/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    738             "Episode 275/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    739             "Episode 276/300, Steps: 174, Reward: -174.0, Epsilon: 0.010\n",
    740             "Episode 277/300, Steps: 120, Reward: -120.0, Epsilon: 0.010\n",
    741             "Episode 278/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    742             "Episode 279/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    743             "Episode 280/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    744             "Episode 281/300, Steps: 180, Reward: -180.0, Epsilon: 0.010\n",
    745             "Episode 282/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    746             "Episode 283/300, Steps: 160, Reward: -160.0, Epsilon: 0.010\n",
    747             "Episode 284/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    748             "Episode 285/300, Steps: 187, Reward: -187.0, Epsilon: 0.010\n",
    749             "Episode 286/300, Steps: 153, Reward: -153.0, Epsilon: 0.010\n",
    750             "Episode 287/300, Steps: 150, Reward: -150.0, Epsilon: 0.010\n",
    751             "Episode 288/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    752             "Episode 289/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    753             "Episode 290/300, Steps: 166, Reward: -166.0, Epsilon: 0.010\n",
    754             "Episode 291/300, Steps: 125, Reward: -125.0, Epsilon: 0.010\n",
    755             "Episode 292/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    756             "Episode 293/300, Steps: 197, Reward: -197.0, Epsilon: 0.010\n",
    757             "Episode 294/300, Steps: 140, Reward: -140.0, Epsilon: 0.010\n",
    758             "Episode 295/300, Steps: 179, Reward: -179.0, Epsilon: 0.010\n",
    759             "Episode 296/300, Steps: 116, Reward: -116.0, Epsilon: 0.010\n",
    760             "Episode 297/300, Steps: 159, Reward: -159.0, Epsilon: 0.010\n",
    761             "Episode 298/300, Steps: 200, Reward: -200.0, Epsilon: 0.010\n",
    762             "Episode 299/300, Steps: 172, Reward: -172.0, Epsilon: 0.010\n",
    763             "Episode 300/300, Steps: 193, Reward: -193.0, Epsilon: 0.010\n"
    764           ]
    765         }
    766       ]
    767     },
    768     {
    769       "cell_type": "code",
    770       "source": [
    771         "# Plot rewards\n",
    772         "plt.plot(rewards_history)\n",
    773         "plt.xlabel(\"Episodes\")\n",
    774         "plt.ylabel(\"Total Reward\")\n",
    775         "plt.title(\"Training Performance\")\n",
    776         "plt.grid(True)\n",
    777         "plt.show()"
    778       ],
    779       "metadata": {
    780         "id": "Yvzo3iueyISN",
    781         "colab": {
    782           "base_uri": "https://localhost:8080/",
    783           "height": 472
    784         },
    785         "outputId": "c9564650-c4a6-4ccd-bd4c-10eb035421a6"
    786       },
    787       "execution_count": null,
    788       "outputs": [
    789         {
    790           "output_type": "display_data",
    791           "data": {
    792             "text/plain": [
    793               "<Figure size 640x480 with 1 Axes>"
    794             ],
    795             "image/png": "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\n"
    796           },
    797           "metadata": {}
    798         }
    799       ]
    800     },
    801     {
    802       "cell_type": "markdown",
    803       "source": [
    804         "**Results**\n",
    805         "\n",
    806         "The training performance indicates that the agent starts with poor rewards around -200, showing no success in the early episodes as it explores the environment. Around episode 100, the agent begins to show improvement, with rewards fluctuating and occasionally exceeding -150. Around episode 200, some progress is made, and the agent achieves higher rewards (closer to -100) in some episodes, but overall it is still quite inconsistent. This means that the agent is learning and improving but has not fully converged on an optimal policy. Additional training, fine-tuning of hyperparameters, or adjustments to the exploration strategy could help stabilize and improve its performance further.\n",
    807         "\n",
    808         "\n"
    809       ],
    810       "metadata": {
    811         "id": "R9XZ5zqr1sW1"
    812       }
    813     },
    814     {
    815       "cell_type": "markdown",
    816       "source": [
    817         "# 5. Evaluation"
    818       ],
    819       "metadata": {
    820         "id": "Q2A8uo0ovX5Q"
    821       }
    822     },
    823     {
    824       "cell_type": "markdown",
    825       "source": [
    826         "The agent is learning, as indicated by our plot, and we managed to reduce the amount of catastrophic forgetting by a lot.\n",
    827         "\n",
    828         "**Reflections**\n",
    829         "\n",
    830         "But, we are quite disappointed with our results. We underestimated the time required for training, not leaving us enough time to fine tune our hyper parameters, or accurately display results. We did however see indications of learning, and managed to improve it from our starting point.\n",
    831         "\n",
    832         "**Possible Improvemens**\n",
    833         "\n",
    834         "We think its possible to improve the model by furher experimenting with the hyper parameters, or maybe implementing double Q-learning to maybe get a more stable learning process (Lecture Q). As we also did not see the model fully converge, we think letting it run for more episodes would also produce a better agent.\n",
    835         "\n",
    836         "To further test the agent, producing more plots, such as a clearly showing the epsilon value over episodes, how the learning rate changes would be benefitial. Perhaps also showing some screenshots from the game to give the reader a better understanding could be helpful, but here we ran into some trouble with not finding correct documentation in time for the older version of gym."
    837       ],
    838       "metadata": {
    839         "id": "7fCDUpb5-8KN"
    840       }
    841     }
    842   ],
    843   "metadata": {
    844     "accelerator": "GPU",
    845     "colab": {
    846       "gpuType": "T4",
    847       "provenance": [],
    848       "include_colab_link": true
    849     },
    850     "kernelspec": {
    851       "display_name": "Python 3",
    852       "name": "python3"
    853     },
    854     "language_info": {
    855       "name": "python"
    856     }
    857   },
    858   "nbformat": 4,
    859   "nbformat_minor": 0
    860 }
	Neural-Computing
	Log \| Files \| Refs \| README \| LICENSE