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A deep reinforcement learning multi-agent algorithm, where a team learns to complete a task and communicate between agents.

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Asynchronous Advantage Actor-Centralized-Critic with Communication (A3C3)

A distributed asynchronous actor-critic algorithm in a multi-agent setting with differentiable communication and a centralized critic.

Check out learned policies here: https://youtu.be/fB71yKcP3iU

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Contains 4 environment suites:

  • POC Suite: Hidden Reward, Navigation, Pursuit, Traffic Intersection
  • MPE Suite: Cooperative Navigation, Cooperative Communication, Cooperative Reference, Tag
  • KiloBot Suite: Light, Join, Split
  • 3d Soccer Simulation Suite: Passing, Keep-Away

Also contains scripts to launch A3C3 and learn policies. Use the requirements.txt to install your dependencies and run the scripts.

Each agent is defined by 3 networks.

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The algorithm is distributed, and multiple workers update the networks.

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The actor network learns a local policy.

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The centralized critic evaluates the policy.

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The communicator network learns a communication protocol between agents.

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A deep reinforcement learning multi-agent algorithm, where a team learns to complete a task and communicate between agents.

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