Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2405.20555v1
• Date: Fri, 31 May 2024 00:41:04 GMT
• Title: Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning
• Authors: Linjiajie Fang, Ruoxue Liu, Jing Zhang, Wenjia Wang, Bing-Yi Jing,
• Abstract summary: In offline reinforcement learning (RL), it is necessary to manage out-of-distribution actions to prevent overestimation of value functions. We propose Diffusion Actor-Critic (DAC) that formulates the Kullback-Leibler (KL) constraint policy iteration as a diffusion noise regression problem. Our approach is evaluated on the D4RL benchmarks and outperforms the state-of-the-art in almost all environments.
• Score: 13.163511229897667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In offline reinforcement learning (RL), it is necessary to manage out-of-distribution actions to prevent overestimation of value functions. Policy-regularized methods address this problem by constraining the target policy to stay close to the behavior policy. Although several approaches suggest representing the behavior policy as an expressive diffusion model to boost performance, it remains unclear how to regularize the target policy given a diffusion-modeled behavior sampler. In this paper, we propose Diffusion Actor-Critic (DAC) that formulates the Kullback-Leibler (KL) constraint policy iteration as a diffusion noise regression problem, enabling direct representation of target policies as diffusion models. Our approach follows the actor-critic learning paradigm that we alternatively train a diffusion-modeled target policy and a critic network. The actor training loss includes a soft Q-guidance term from the Q-gradient. The soft Q-guidance grounds on the theoretical solution of the KL constraint policy iteration, which prevents the learned policy from taking out-of-distribution actions. For critic training, we train a Q-ensemble to stabilize the estimation of Q-gradient. Additionally, DAC employs lower confidence bound (LCB) to address the overestimation and underestimation of value targets due to function approximation error. Our approach is evaluated on the D4RL benchmarks and outperforms the state-of-the-art in almost all environments. Code is available at \href{https://github.com/Fang-Lin93/DAC}{\texttt{github.com/Fang-Lin93/DAC}}.

Related papers

• Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning [5.012314384895537]
  In offline reinforcement learning, a policy is learned using a static dataset in the absence of costly feedback from the environment. We propose Constrained Latent Action Policies (C-LAP) which learns a generative model of the joint distribution of observations and actions.
  arXiv  Detail & Related papers  (2024-11-07T09:35:22Z)
• Diffusion Policies creating a Trust Region for Offline Reinforcement Learning [66.17291150498276]
  We introduce a dual policy approach, Diffusion Trusted Q-Learning (DTQL), which comprises a diffusion policy for pure behavior cloning and a practical one-step policy. DTQL eliminates the need for iterative denoising sampling during both training and inference, making it remarkably computationally efficient. We show that DTQL could not only outperform other methods on the majority of the D4RL benchmark tasks but also demonstrate efficiency in training and inference speeds.
  arXiv  Detail & Related papers  (2024-05-30T05:04:33Z)
• Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization [55.97310586039358]
  Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality. We propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO) Specifically, we introduce the Q-weighted variational loss, which can be proved to be a tight lower bound of the policy objective in online RL under certain conditions. We also develop an efficient behavior policy to enhance sample efficiency by reducing the variance of the diffusion policy during online interactions.
  arXiv  Detail & Related papers  (2024-05-25T10:45:46Z)
• Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning [11.0460569590737]
  This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL) We show that an SDE has a solution that we can use to calculate the log probability of the policy, yielding an entropy regularizer that improves the exploration of offline datasets. By combining the entropy-regularized diffusion policy with Q-ensembles in offline RL, our method achieves state-of-the-art performance on most tasks in D4RL benchmarks.
  arXiv  Detail & Related papers  (2024-02-06T15:34:30Z)
• Learning a Diffusion Model Policy from Rewards via Q-Score Matching [93.0191910132874]
  We present a theoretical framework linking the structure of diffusion model policies to a learned Q-function. We propose a new policy update method from this theory, which we denote Q-score matching.
  arXiv  Detail & Related papers  (2023-12-18T23:31:01Z)
• Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning [57.83919813698673]
  Projected Off-Policy Q-Learning (POP-QL) is a novel actor-critic algorithm that simultaneously reweights off-policy samples and constrains the policy to prevent divergence and reduce value-approximation error. In our experiments, POP-QL not only shows competitive performance on standard benchmarks, but also out-performs competing methods in tasks where the data-collection policy is significantly sub-optimal.
  arXiv  Detail & Related papers  (2023-11-25T00:30:58Z)
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning [70.20191211010847]
  Offline reinforcement learning (RL) aims to learn an optimal policy using a previously collected static dataset. We introduce Diffusion Q-learning (Diffusion-QL) that utilizes a conditional diffusion model to represent the policy. We show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks.
  arXiv  Detail & Related papers  (2022-08-12T09:54:11Z)
• 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)
• Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors [13.534873779043478]
  We present a distributional soft actor-critic (DSAC) algorithm to improve the policy performance by mitigating Q-value overestimations. We evaluate DSAC on the suite of MuJoCo continuous control tasks, achieving the state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-01-09T02:27:18Z)

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.