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REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement
Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies, however, are often defined as direct mappings from an observed state to an action or action distribution, requiring a single feed-forward network t
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