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<!doctype html>
<html class="no-js" lang="">
<head>
<meta charset="utf-8">
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<title></title>
<meta name="description" content="">
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<body>
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background-size: 40px 40px;
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border: 1px solid black;
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<aside>
<ul>
<li>
P1
</li>
<li>P2</li>
<li class="active">P3</li>
</ul>
</aside>
<div class="gradient-backdrop">
<h1>Project 3: Collaboration</h1>
</div>
<div id="content">
<section>
<h2>Approach</h2>
<p>
When approaching the problem of solving the "Tennis" environment, we relied heavily on the
knowledge and intuition gained from earlier chapters from the course.
We therefore decided to base our implementation on the MADDPG algorithm, used to solve the "Pendulum" environment.
We found that the agent was able to complete the task and achieve a score greater than 0.5 in 417 episodes with a final score of 0.53.
Please see the plots and tables below for more information.
</p>
</section>
<section>
<h2>
Algorithm and Architecture
</h2>
The DDPG algorithm on which MADDPG is based, uses an two neural networks. The first network is called the Actor, and is used to map states to actions.
The second network is referred to as the Critic, and maps state-action pairs to Q-values.
The Actor produces an action given the current state of the environment. The critic then produces TD error signal, which
drives learning in both the actor and the critic.
This approach allows us to optimize a policy, with a continous action-space, in a deterministic fashion.
Target networks for both the Actor and Critic networks are used in order to avoid correlation excessive
when calculating the loss factor.
The important diffetence with MADDPG is that all agents use a shared Replay Buffer for centralized training and decentralized execution.
</br>
To achieve our results, we used the following hyper parameters:
<ul>
<li>BUFFER_SIZE = int(1e6)</li>
<li>BATCH_SIZE = 512</li>
<li>GAMMA = 0.99</li>
<li>TAU = 1e-1</li>
<li>LR_ACTOR = 1e-3</li>
<li>LR_CRITIC = 1e-3</li>
<li>WEIGHT_DECAY = 0</li>
</ul>
We also played around with the hyper parameters, and found that lowering the "tau" interpolation parameter greatly increased the results of the agent(s).
We also lowered the learning rate on the Agent to 1e-3 to achieve our final results.
Further more, the neural networks used to represent the actor and critic components both had 3 fully connected layers, and utilized a combination of Rectified linear unit (ReLU) and tanh activation functions.
The number of neurons used in the hidden layers for the Actor and Critic networks were 400, 300 and 128, 256 respectively.
</section>
<section class="container">
<h2>MADDPG</h2>
Environment solved in 417 episodes! Average Score: 0.53.
<div class="row">
<div class="col">
<div class="img-container"><img src="./img/graph.png" alt="Vanilla DQN"></div>
</div>
</div>
</section>
<section>
<h2>Obstacles & Future improvements</h2>
<p>
The next step would be to try implementing D4PG as well as a more distributed version, with parallel training.
We would also like to attempt a similar approach in trying to solve an environment with even more agents.
</p>
</section>
<section>
<h2>References</h2>
<ul>
<li>
A link to the original paper on MADDPG can be found <a href="https://arxiv.org/abs/1706.02275">here</a>.
</li>
</ul>
</section>
</div>
</body>
</html>