README.md

Soft Actor-Critic

We provide the following three multi-agent extensions to the Soft Actor-Critic (SAC) algorithm.

• ff-ISAC
• ff-MASAC
• ff-HASAC

ISAC is an implementation following the independent learners MARL paradigm while MASAC is an implementation that follows the centralised training with decentralised execution paradigm by having a centralised critic during training. HASAC follows the heterogeneous agent learning paradigm through sequential policy updates. The ff prefix to the algorithm names indicate that the algorithms use MLP-based policy networks.

Relevant papers

• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
• Robust Multi-Agent Control via Maximum Entropy Heterogeneous-Agent Reinforcement Learning