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Found 3 papers, 1 papers with code
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
To address this issue, we propose a novel self-interpretable structure, named Backbone Extract Tree (BET), to better explain the agent's behavior by identify the error-prone states.
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning
We then design a fidelity-induced mechanism by integrate a fidelity measurement into the reinforcement learning feedback.
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient
Empirically, we evaluate MAPPG on the well-known matrix game and differential game, and verify that MAPPG can converge to the global optimum for both discrete and continuous action spaces.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
