Multi‑Agent Distributed Reinforcement Learning for grid Environment

In this project, I’ve Implemented a grid environment with 2 agents and 2 goal states. The agents have to learn to reach the goal states by receiving the maximum reward and avoiding obstacles. The environment is shown below

Elements of Environment:

• Agents: Blue Squares
• Obstacles: Red Squares
• Goal States: Green Squares

Training

After training each agent lonely with the sarsa algorithm, I implemented several distributed algorithms like:

• Distributed On-Policy algorithms like SARSA
• Min-Max Q-Learning
• Belief Based Algorithm
• Distributed Actor-Critic

Results

The average Reward during the learning episodes for SARSA, Min-Max Q-Learning, and Belief-Based learning is shown Below:

• Average Reward during the learning episodes for SARSA

• Average Reward during the learning episodes for SARSA

• Average Reward during the learning episodes for Min-Max Q-Learning

• Average Reward during the learning episodes for Belief Based Algorithms