# Addressing Function Approximation Error in Actor-Critic Methods

arXiv: https://arxiv.org/abs/1802.09477 · HTML: https://mlsanity.com/paper/1802.09477/

## Key facts

- Cited by 2,417 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

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## Abstract (arXiv)

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.