Deep RL agents' best experienced trajectories are 2-3 times better than their learned policy's average return, suggesting exploitation and optimization issues dominate exploration challenges.
Bellemare, Will Dabney, and R \' e mi Munos
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Is Exploration or Optimization the Problem for Deep Reinforcement Learning?
Deep RL agents' best experienced trajectories are 2-3 times better than their learned policy's average return, suggesting exploitation and optimization issues dominate exploration challenges.