The Least Restriction for Offline Reinforcement Learning

07/05/2021
by   Zizhou Su, et al.
0

Many practical applications of reinforcement learning (RL) constrain the agent to learn from a fixed offline dataset of logged interactions, which has already been gathered, without offering further possibility for data collection. However, commonly used off-policy RL algorithms, such as the Deep Q Network and the Deep Deterministic Policy Gradient, are incapable of learning without data correlated to the distribution under the current policy, making them ineffective for this offline setting. As the first step towards useful offline RL algorithms, we analysis the reason of instability in standard off-policy RL algorithms. It is due to the bootstrapping error. The key to avoiding this error, is ensuring that the agent's action space does not go out of the fixed offline dataset. Based on our consideration, a creative offline RL framework, the Least Restriction (LR), is proposed in this paper. The LR regards selecting an action as taking a sample from the probability distribution. It merely set a little limit for action selection, which not only avoid the action being out of the offline dataset but also remove all the unreasonable restrictions in earlier approaches (e.g. Batch-Constrained Deep Q-Learning). In the further, we will demonstrate that the LR, is able to learn robustly from different offline datasets, including random and suboptimal demonstrations, on a range of practical control tasks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

11/08/2021

Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning

In real world, affecting the environment by a weak policy can be expensi...
06/21/2021

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

We consider the offline reinforcement learning (RL) setting where the ag...
06/03/2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Off-policy reinforcement learning aims to leverage experience collected ...
11/14/2020

PLAS: Latent Action Space for Offline Reinforcement Learning

The goal of offline reinforcement learning is to learn a policy from a f...
02/07/2021

An Analysis of Frame-skipping in Reinforcement Learning

In the practice of sequential decision making, agents are often designed...
10/26/2021

The Difficulty of Passive Learning in Deep Reinforcement Learning

Learning to act from observational data without active environmental int...
10/16/2020

Learning Dexterous Manipulation from Suboptimal Experts

Learning dexterous manipulation in high-dimensional state-action spaces ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.