PLAS: Latent Action Space for Offline Reinforcement Learning

Understand

The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment.

• This setting will be an increasingly more important paradigm for real-world applications of reinforcement learning such as robotics, in which data collection is slow and potentially dangerous.
• Existing off-policy algorithms have limited performance on static datasets due to extrapolation errors from out-of-distribution actions.
• This leads to the challenge of constraining the policy to select actions within the support of the dataset during training.