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Poster
Direct Advantage Estimation
Hsiao-Ru Pan · Nico Gürtler · Alexander Neitz · Bernhard Schölkopf

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #336
in Poster Session 5 »

The predominant approach in reinforcement learning is to assign credit to actions based on the expected return. However, we show that the return may depend on the policy in a way which could lead to excessive variance in value estimation and slow down learning. Instead, we show that the advantage function can be interpreted as causal effects and shares similar properties with causal representations. Based on this insight, we propose Direct Advantage Estimation (DAE), a novel method that can model the advantage function and estimate it directly from on-policy data while simultaneously minimizing the variance of the return without requiring the (action-)value function. We also relate our method to Temporal Difference methods by showing how value functions can be seamlessly integrated into DAE. The proposed method is easy to implement and can be readily adapted by modern actor-critic methods. We evaluate DAE empirically on three discrete control domains and show that it can outperform generalized advantage estimation (GAE), a strong baseline for advantage estimation, on a majority of the environments when applied to policy optimization.

Keywords: Reinforcement Learning · actor-critic · causal effect · advantage function
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Hsiao-Ru Pan (Max Planck Institute for Intelligent Systems)
Nico Gürtler (Max Planck Institute for Intelligent Systems, Tübingen)
Alexander Neitz (DeepMind)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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