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<h1 id="bipedal-walker">Bipedal Walker</h1>
<p>This is how to teach a bot to walk on two feet.</p>
<p>Bipedal Walker is an <a href="https://gymnasium.farama.org/environments/box2d/bipedal_walker/">OpenAI Gym</a> environment where an agent learns to control a bipedal 2D character to reach the end of an obstacle course. What makes this challenging is that the agent only receives limbs coordinates along with the Lidar information. The agent has to learn to balance, walk, run, jump on its own without any human intervention.</p>
<p>There are two versions:</p>
<ul>
<li>
<p>Normal, with slightly uneven terrain.</p>
</li>
<li>
<p>Hardcore, with ladders, stumps, pitfalls.</p>
</li>
</ul>
<p>To solve the normal version, you need to get 300 points in 1600 time steps. To solve the hardcore version, you need 300 points in 2000 time steps.</p>
<p>The code in this repo solves Bipedal Walker V3 in normal mode (<code>hardcore=False</code>).</p>
<h2 id="prerequisites-and-recommended-tools">Prerequisites and Recommended Tools</h2>
<ul>
<li><a href="https://conda.io/projects/conda/en/latest/user-guide/install/index.html">Conda</a> installed on your system</li>
<li>Visual Studio Code</li>
</ul>
<h2 id="setup-instructions">Setup Instructions</h2>
<p>Follow the steps below to set up your environment and install the necessary dependencies.</p>
<h5 id="1-create-a-conda-environment">1. Create a Conda Environment</h5>
<p>First, create a new Conda environment with Python 3.11:</p>
<pre class="hljs"><code><div>conda create -n gym-env python=3.11 -y
</div></code></pre>
<h5 id="2-activate-the-newly-created-environment">2. Activate the newly created environment</h5>
<pre class="hljs"><code><div>conda activate gym-env
</div></code></pre>
<h5 id="3-install-the-required-packages">3. Install the required packages</h5>
<pre class="hljs"><code><div>conda install \
swig gymnasium-box2d \
stable-baselines3 tensorboard \
plotly scikit-learn optuna -y
</div></code></pre>
<h3 id="project-structure">Project Structure</h3>
<ul>
<li><code>bipedal_train.py</code>: Train the agent with PPO default parameters.</li>
<li><code>bipedal_eval.py</code>: Evaluate the agent using a previously saved model.</li>
<li><code>optuna_ppo.py</code>: Auto-tune PPO using <a href="https://optuna.org/">OPTUNA</a>.</li>
<li><code>optuna_ddpg.py</code>: Auto-tune DDPG using <a href="https://optuna.org/">OPTUNA</a>.</li>
<li><code>bipedal_wandb_ppo.py</code>: Train / evaluate the agent with tuned hyper-parameters + <a href="https://wandb.ai/">W&B</a> integration.</li>
<li><code>bipedal_wandb_ddpg.py</code>: Train / evaluate the agent with tuned hyper-parameters + <a href="https://wandb.ai/">W&B</a> integration.</li>
<li><code>record_video.py</code>: Record a video of the trained agent.</li>
</ul>
<h3 id="tensorboard-visualization">Tensorboard Visualization</h3>
<pre class="hljs"><code><div>tensorboard --logdir=./training/logs
</div></code></pre>
<h1 id="custom-environment">Custom Environment</h1>
<p>The code for our customized environment can be found in the <code>modified_env</code> folder as a separate implementation.</p>
<h3 id="description-of-changes">Description of changes:</h3>
<ol>
<li>For new angular velocity at joints and hip, changes were made at line 34 and 35. The source code of this environment can be in <code>/modified_env/custom_env/bipedal_walker_angular.py</code></li>
</ol>
<pre class="hljs"><code><div>SPEED_HIP = <span class="hljs-number">8</span> <span class="hljs-comment"># default value: 4</span>
SPEED_KNEE = <span class="hljs-number">12</span> <span class="hljs-comment"># default value: 6</span>
</div></code></pre>
<p>To use this environment for training, you can run:</p>
<pre class="hljs"><code><div>python ./modified_env/bipedal_custom_angular.py
</div></code></pre>
<p>To use this environment for evaluation, you can run:</p>
<pre class="hljs"><code><div>python ./modified_env/eval_angular.py
</div></code></pre>
<ol start="2">
<li>For new reward scheme, new equation was added at line 575. The source code of this environment is in <code>/modified_env/custom_env/bipedal_walker_vel.py</code></li>
</ol>
<pre class="hljs"><code><div><span class="hljs-comment"># reward will be given to velocity</span>
<span class="hljs-keyword">if</span> vel.x > <span class="hljs-number">5.0</span>:
<span class="hljs-comment"># normalize it to scale[-1, 1]</span>
reward += <span class="hljs-number">0.08</span> * <span class="hljs-number">0.3</span> * vel.x * (VIEWPORT_W / SCALE) / FPS
</div></code></pre>
<p>To use this environment for training, you can run:</p>
<pre class="hljs"><code><div>python ./modified_env/bipedal_custom_vel.py
</div></code></pre>
<p>To use this environment for evaluation, you can run:</p>
<pre class="hljs"><code><div>python ./modified_env/eval_vel.py
</div></code></pre>
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