ISA uses mutual information between actions and state changes to define each agent's influence scope, then uses it for credit assignment and count-based exploration in sparse-reward MARL.
Changing the environment based on em- powerment as intrinsic motivation
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Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning
ISA uses mutual information between actions and state changes to define each agent's influence scope, then uses it for credit assignment and count-based exploration in sparse-reward MARL.