LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning

Understand

Efficient exploration is important for reinforcement learners to achieve high rewards.

• In multi-agent systems, coordinated exploration and behaviour is critical for agents to jointly achieve optimal outcomes.
• In this paper, we introduce a new general framework for improving coordination and performance of multi-agent reinforcement learners (MARL).
• Our framework, named Learnable Intrinsic-Reward Generation Selection algorithm (LIGS) introduces an adaptive learner, Generator that observes the agents and learns to construct intrinsic rewards online that coordinate the agents' joint exploration and joint behaviour.