Designing Internal Reward of Reinforcement Learning Agents in Multi-Step Dilemma Problem
Yoshihiro Ichikawa and Keiki Takadama
The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
This paper proposes the reinforcement learning agent that estimates internal rewards using external rewards in order to avoid conflict in multi-step dilemma problem. Intensive simulation results have revealed that the agent succeeds in avoiding local convergence and obtains a behavior policy for reaching a higher reward by updating the Q-value using the value that is subtracted the average reward from an external reward.
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