multi-agent-rl

Multi-agent reinforcement learning addresses credit assignment and non-stationarity by using centralized training with decentralized execution (CTDE) to stabilize learning. This approach leverages centralized critics or value factorization to learn coordinated policies for multiple agents in shared environments.

QMIX and MADDPG differ in their approach to multi-agent coordination: QMIX uses value factorization for cooperative settings, while MADDPG employs centralized critics for cooperative, competitive, and mixed-agent scenarios. Both support centralized training with decentralized execution to ensure stable learning.

Centralized training with decentralized execution (CTDE) in PyTorch involves training agents with access to global state information, then executing policies based only on local observations. Implementations use value-factorization methods like QMIX or centralized critics like MADDPG to demonstrate training and evaluation.

Value factorization like QMIX is typically used for cooperative multi-agent RL scenarios, while centralized critics like MADDPG handle cooperative, competitive, and mixed-agent settings. Choose value factorization for purely cooperative tasks and centralized critics for environments with adversarial or mixed agent interactions.

Multi-agent RL algorithms support traffic coordination, robotics teams, and game AI scenarios. They enable multiple agents to learn concurrently in shared environments, addressing coordination challenges through CTDE, QMIX, and MADDPG implementations to develop effective decentralized execution policies.