MAGIC estimates multi-step action effects between agents with counterfactual interventions, gates them by advantage, and converts them to intrinsic rewards, yielding 26.9% and 10.1% relative gains on MPE and SMAC benchmarks.
Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems , pages=
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MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning
MAGIC estimates multi-step action effects between agents with counterfactual interventions, gates them by advantage, and converts them to intrinsic rewards, yielding 26.9% and 10.1% relative gains on MPE and SMAC benchmarks.