Multiobjective Reinforcement Learning Using Adaptive Dynamic Programming And Reservoir Computing
نویسندگان
چکیده
This paper introduces a multiobjective reinforcement learning approach which is suitable for large state and action spaces. The approach is based on actorcritic design and reservoir computing. A single reservoir estimates several utilities simultaneously and provides their gradients that are required for the actor enabling an agent to adapt its behavior in presence of several sources of rewards. We describe the approach in theoretical terms, supported by simulation results.
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