Papers › Addressing Function Approximation Error in Actor-Critic Methods

Addressing Function Approximation Error in Actor-Critic Methods

26 Feb 2018ICML 2018 7arXiv:1802.09477archive 2025-07-28

Scott Fujimoto, Herke van Hoof, David Meger

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.

Code

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Tasks

Results from the paper archive 2025-07-28

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Methods

Introduced by this paper: TD3, Target Policy Smoothing

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