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Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto · Herke van Hoof · David Meger

Thu Jul 12 06:20 AM -- 06:30 AM (PDT) @ A1

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.

Author Information

Scott Fujimoto (McGill University)
Herke van Hoof (McGill University)
David Meger (McGill University)

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