Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

1 Oct 2019  ·  Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine ·

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that utilizes only simple and convergent maximum likelihood loss functions, while also being able to leverage off-policy data. Our proposed approach, which we refer to as advantage-weighted regression (AWR), consists of two standard supervised learning steps: one to regress onto target values for a value function, and another to regress onto weighted target actions for the policy. The method is simple and general, can accommodate continuous and discrete actions, and can be implemented in just a few lines of code on top of standard supervised learning methods. We provide a theoretical motivation for AWR and analyze its properties when incorporating off-policy data from experience replay. We evaluate AWR on a suite of standard OpenAI Gym benchmark tasks, and show that it achieves competitive performance compared to a number of well-established state-of-the-art RL algorithms. AWR is also able to acquire more effective policies than most off-policy algorithms when learning from purely static datasets with no additional environmental interactions. Furthermore, we demonstrate our algorithm on challenging continuous control tasks with highly complex simulated characters.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
OpenAI Gym Ant-v2 AWR Average Return 5067 # 1
OpenAI Gym HalfCheetah-v2 AWR Average Return 9136 # 1
OpenAI Gym Hopper-v2 AWR Average Return 3405 # 1
OpenAI Gym Humanoid-v2 AWR Average Return 4996 # 1
OpenAI Gym LunarLander-v2 AWR Average Return 229 # 2
OpenAI Gym Walker2d-v2 AWR Average Return 5813 # 1


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