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Poster
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis · Harris Chan · Stephen Zhao · Bradly Stadie · Jimmy Ba

Thu Jul 16 06:00 AM -- 06:45 AM & Thu Jul 16 05:00 PM -- 05:45 PM (PDT) @
in Poster Session 41 »

What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a useful learning signal, we argue that the agent should not pursue unobtainable goals. Instead, it should set its own intrinsic goals that maximize the entropy of the historical achieved goal distribution. We propose to optimize this objective by having the agent pursue past achieved goals in sparsely explored areas of the goal space, which focuses exploration on the frontier of the achievable goal set. We show that our strategy achieves an order of magnitude better sample efficiency than the prior state of the art on long-horizon multi-goal tasks including maze navigation and block stacking.

Keywords: Deep Reinforcement Learning · Transfer and Multitask Learning · Reinforcement Learning · Reinforcement Learning - Deep RL

Author Information

Silviu Pitis (University of Toronto)
Harris Chan (University of Toronto, Vector Institute)
Stephen Zhao (University of Toronto)
Bradly Stadie (Vector Institute)
Jimmy Ba (University of Toronto)

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