multicraft

This is our experiments on two different RL approaches in solving a multi-agent setting, in particular, a Tag scenario. The environment and scenario we used is from the Multi-Agent Particle Environments (MPE).

Dependencies

• Python (3.5.4)
• OpenAI gym (0.10.5)
• Tensorflow (1.8.0)
• numpy (1.14.5)
• torch (1.3.1)

Running Experiments

• under /experiment/ directory, execute train.py: this executes two learning algorithms, SARSA and DDPG respectively, and trains 2000 episodes each. By default, the scenario is rendered every 100 episodes.

• The motivation for our experiments is to compare the performance of learned agents against that of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and to validate MADDPG as well.

• To run our MADDPG experiment (based on https://github.com/openai/maddpg): under /maddpg/experiments/ directory, execute train.py

Results

• SARSAR and DDPG experiments each produces a result benchmark.csv, stored at /experiment/sarsa_out and /experiment/ddpg_out espectively

• MADDPG's experiment result (benchmark.csv) is stored at /maddpg/experiments/maddpg_out