Multi-Agent Reinforcement Learning Algorithms
Project Overview
This repository implements several modern reinforcement learning algorithms with modular and extensible architecture. Designed with future support for multi-agent environments in mind, it includes training pipelines for TD3, DDPG, PPO, and SAC.
Technologies Used
- PyTorch
- Gym Environments
- Continuous Control Algorithms
- Replay Buffers and Target Networks
Key Features
- Stable baselines for single-agent learning
- Ready for multi-agent extension
- Clear experiment logging and training plots
- Modular agent architecture and configuration
Research Contributions
- Clean implementation of on-policy and off-policy algorithms
- Experimental framework for algorithm comparison
- Baseline for integration with exploration and coordination research