RL-Unity

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Implementation of STOA Deep Reinforcement Learning (DRL) algorithms in the Unity Engine.

Algorithms:

1. Value Based Method - Deep Q Network (DQN)
2. Policy Based Method - Deep Deterministic Policy Gradient (DDPG)
3. Multi Agent Reinforcement Learning using MADDPG

Outputs:

Value Based Method - DQN

In this Unity environment, the goal of the agent is to pick up yellow bananas while avoiding blue bananas.

Rolling Scores

Policy Based Method - DDPG

In this Unity environment, the goal of the agent is to move the double-jointed arm to the target location indicated by the torquoise sphere. This video demonstrates a more practical approach of the Reacher Unity environment.

Rolling Scores

Multi-Agent RL - MADDPG

In this Unity environment, the goal of the agent is to maximize the rally between the two tennis agents, i.e. as the two agents pass the ball to each other without dropping, the higher the reward.

Rolling Scores