Stable Offline Value Function Learning with Bisimulation-based Representations

• URL: http://arxiv.org/abs/2410.01643v2
• Date: Sat, 2 Nov 2024 19:33:27 GMT
• Title: Stable Offline Value Function Learning with Bisimulation-based Representations
• Authors: Brahma S. Pavse, Yudong Chen, Qiaomin Xie, Josiah P. Hanna,
• Abstract summary: In reinforcement learning, offline value function learning is used to estimate the expected discounted return from each state when taking actions according to a fixed target policy. It is critical to stabilize value function learning by explicitly shaping the state-action representations. We introduce a bisimulation-based algorithm called kernel representations for offline policy evaluation (KROPE)
• Score: 13.013000247825248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In reinforcement learning, offline value function learning is the procedure of using an offline dataset to estimate the expected discounted return from each state when taking actions according to a fixed target policy. The stability of this procedure, i.e., whether it converges to its fixed-point, critically depends on the representations of the state-action pairs. Poorly learned representations can make value function learning unstable, or even divergent. Therefore, it is critical to stabilize value function learning by explicitly shaping the state-action representations. Recently, the class of bisimulation-based algorithms have shown promise in shaping representations for control. However, it is still unclear if this class of methods can stabilize value function learning. In this work, we investigate this question and answer it affirmatively. We introduce a bisimulation-based algorithm called kernel representations for offline policy evaluation (KROPE). KROPE uses a kernel to shape state-action representations such that state-action pairs that have similar immediate rewards and lead to similar next state-action pairs under the target policy also have similar representations. We show that KROPE: 1) learns stable representations and 2) leads to lower value error than baselines. Our analysis provides new theoretical insight into the stability properties of bisimulation-based methods and suggests that practitioners can use these methods for stable and accurate evaluation of offline reinforcement learning agents.

Related papers

• Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation [22.129001951441015]
  Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation. This reliance results in data inefficiency as maintaining a state-action-value function in high-dimensional action spaces is challenging. We present an efficient approach that utilizes only a state-value function as the critic for off-policy deep reinforcement learning.
  arXiv  Detail & Related papers  (2024-03-07T12:45:51Z)
• Confidence-Conditioned Value Functions for Offline Reinforcement Learning [86.59173545987984]
  We propose a new form of Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability. We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence.
  arXiv  Detail & Related papers  (2022-12-08T23:56:47Z)
• Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning [105.70602423944148]
  We propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. Instead of aligning this imagined state with a real state returned by the environment, VCR applies a $Q$-value head on both states and obtains two distributions of action values. It has been demonstrated that our methods achieve new state-of-the-art performance for search-free RL algorithms.
  arXiv  Detail & Related papers  (2022-06-25T03:02:25Z)
• Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism [65.46524775457928]
  offline reinforcement learning seeks to utilize offline/historical data to optimize sequential decision-making strategies. We study the statistical limits of offline reinforcement learning with linear model representations.
  arXiv  Detail & Related papers  (2022-03-11T09:00:12Z)
• Offline Reinforcement Learning with Implicit Q-Learning [85.62618088890787]
  Current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy. We propose an offline RL method that never needs to evaluate actions outside of the dataset. This method enables the learned policy to improve substantially over the best behavior in the data through generalization.
  arXiv  Detail & Related papers  (2021-10-12T17:05:05Z)
• What are the Statistical Limits of Offline RL with Linear Function Approximation? [70.33301077240763]
  offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of sequential decision making strategies. This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning.
  arXiv  Detail & Related papers  (2020-10-22T17:32:13Z)
• Inverse Policy Evaluation for Value-based Sequential Decision-making [10.188967035477217]
  Value-based methods for reinforcement learning lack generally applicable ways to derive behavior from a value function. We show that inverse policy evaluation, combined with an approximate value iteration algorithm, is a feasible method for value-based control.
  arXiv  Detail & Related papers  (2020-08-26T01:31:38Z)
• Representations for Stable Off-Policy Reinforcement Learning [37.561660796265]
  Reinforcement learning with function approximation can be unstable and even divergent. We show that non-trivial state representations under which the canonical TD algorithm is stable, even when learning off-policy. We conclude by empirically demonstrating that these stable representations can be learned using gradient descent.
  arXiv  Detail & Related papers  (2020-07-10T17:55:54Z)
• Offline Contextual Bandits with Overparameterized Models [52.788628474552276]
  We ask whether the same phenomenon occurs for offline contextual bandits. We show that this discrepancy is due to the emphaction-stability of their objectives. In experiments with large neural networks, this gap between action-stable value-based objectives and unstable policy-based objectives leads to significant performance differences.
  arXiv  Detail & Related papers  (2020-06-27T13:52:07Z)

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.