Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2202.11566v1
• Date: Wed, 23 Feb 2022 15:27:16 GMT
• Title: Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
• Authors: Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, Zhaoran Wang
• Abstract summary: Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. We propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints.
• Score: 125.8224674893018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problem by penalizing the Q-values of OOD actions or constraining the trained policy to be close to the behavior policy. Nevertheless, such methods typically prevent the generalization of value functions beyond the offline data and also lack precise characterization of OOD data. In this paper, we propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints. Specifically, PBRL conducts uncertainty quantification via the disagreement of bootstrapped Q-functions, and performs pessimistic updates by penalizing the value function based on the estimated uncertainty. To tackle the extrapolating error, we further propose a novel OOD sampling method. We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL. Extensive experiments on D4RL benchmark show that PBRL has better performance compared to the state-of-the-art algorithms.

Related papers

• Strategically Conservative Q-Learning [89.17906766703763]
  offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility. The major difficulty in offline RL is mitigating the impact of approximation errors when encountering out-of-distribution (OOD) actions. We propose a novel framework called Strategically Conservative Q-Learning (SCQ) that distinguishes between OOD data that is easy and hard to estimate.
  arXiv  Detail & Related papers  (2024-06-06T22:09:46Z)
• Behavior Proximal Policy Optimization [14.701955559885615]
  offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly. Online on-policy algorithms are naturally able to solve offline RL. We propose Behavior Proximal Policy Optimization (BPPO), which solves offline RL without any extra constraint or regularization.
  arXiv  Detail & Related papers  (2023-02-22T11:49:12Z)
• Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation [38.95482624075353]
  We introduce gradient penalty over the learned value function to tackle the exploding Q-functions. We then relax the closeness constraints towards non-optimal actions with critic weighted constraint relaxation. Experimental results show that the proposed techniques effectively tame the non-optimal trajectories for policy constraint offline RL methods.
  arXiv  Detail & Related papers  (2022-10-19T11:22:36Z)
• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble [16.92791301062903]
  We propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution. Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning.
  arXiv  Detail & Related papers  (2021-10-04T16:40:13Z)
• BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning [14.432131909590824]
  Offline Reinforcement Learning aims to train effective policies using previously collected datasets. Standard off-policy RL algorithms are prone to overestimations of the values of out-of-distribution (less explored) actions. We improve the behavior regularized offline reinforcement learning and propose BRAC+.
  arXiv  Detail & Related papers  (2021-10-02T23:55:49Z)
• Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning [63.53407136812255]
  Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. Existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs and down-weights their contribution in the training objectives accordingly.
  arXiv  Detail & Related papers  (2021-05-17T20:16:46Z)
• Instabilities of Offline RL with Pre-Trained Neural Representation [127.89397629569808]
  In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated. Recent theoretical advances have shown that such sample-efficient offline RL is indeed possible provided certain strong representational conditions hold. This work studies these issues from an empirical perspective to gauge how stable offline RL methods are.
  arXiv  Detail & Related papers  (2021-03-08T18:06:44Z)
• Continuous Doubly Constrained Batch Reinforcement Learning [93.23842221189658]
  We propose an algorithm for batch RL, where effective policies are learned using only a fixed offline dataset instead of online interactions with the environment. The limited data in batch RL produces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data. We propose to mitigate this issue via two straightforward penalties: a policy-constraint to reduce this divergence and a value-constraint that discourages overly optimistic estimates.
  arXiv  Detail & Related papers  (2021-02-18T08:54:14Z)
• Is Pessimism Provably Efficient for Offline RL? [104.00628430454479]
  We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. We propose a pessimistic variant of the value iteration algorithm (PEVI), which incorporates an uncertainty quantifier as the penalty function.
  arXiv  Detail & Related papers  (2020-12-30T09:06:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.