Parallel Deep Reinforcement Learning with Federated Learning Framework

The purpose of this project is to assess the effect of parallel training of multiple Deep Reinforcement Learning agents using the Federated Averaging (FedAVG) algorithm -- after training the agents for specific timesteps, all of the Deep Q Network models are aggregated by taking the average of their parameters and subsequently the averaged model will be set for all of the agents for more training rounds.

Environments

• CartPole
• Lunar Lander
• Super Mario Bros

Deep Reinforcement Learning Methods

• Deep Q Network
• Double Deep Q Network

Experiments

3 DQN Agents on Cartpole Environment

3 DQN Agents on Lunar Lander Environment

4 DDQN Agents on Super Mario Bros 1-1 to 1-4

Env 1-1	Env 1-2
Env 1-3	Env 1-4