Addressing Function Approximation Error in Actor-Critic Methods

ICML 2018  ·  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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
OpenAI Gym Ant-v4 TD3 Average Return 5942.55 # 2
OpenAI Gym HalfCheetah-v4 TD3 Average Return 12026.73 # 3
OpenAI Gym Hopper-v4 TD3 Average Return 3319.98 # 2
OpenAI Gym Humanoid-v4 TD3 Average Return 198.44 # 4