Weighted Average Composition of Deep Reinforcement Learning Agents in Discrete Action Problems

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  • 深層強化学習Agentの離散行動空間タスクにおける重み付き結合

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Composition of pre-trained agents is gathering attention in the field of reinforcement learning since this approach allows us to construct an agent that solves a new task by combining multiple pre-trained agents that solve different tasks. In this study, we extend an existing method that composes pre-trained agents with simple average and propose a new method that composes pre-trained agents with a weighted average. The proposed method enables us to solve a new task whose reward function is expressed as the linear combination of base tasks. We verify the effectiveness of the proposed method by CartPole control and traffic signal control problems.

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