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1. Efficient Online Reinforcement Learning for Diffusion Policy

   Ma, Haitong; Chen, Tianyi; Wang, Kai; Li, Na; Dai, Bo (July 2025, PMLR)

   Diffusion policies have achieved superior performance in imitation learning and offline reinforcement learning (RL) due to their rich expressiveness. However, the conventional diffusion training procedure requires samples from target distribution, which is impossible in online RL since we cannot sample from the optimal policy. Backpropagating policy gradient through the diffusion process incurs huge computational costs and instability, thus being expensive and not scalable. To enable efficient training of diffusion policies in online RL, we generalize the conventional denoising score matching by reweighting the loss function. The resulting Reweighted Score Matching (RSM) preserves the optimal solution and low computational cost of denoising score matching, while eliminating the need to sample from the target distribution and allowing learning to optimize value functions. We introduce two tractable reweighted loss functions to solve two commonly used policy optimization problems, policy mirror descent and max-entropy policy, resulting in two practical algorithms named Diffusion Policy Mirror Descent (DPMD) and Soft Diffusion Actor-Critic (SDAC). We conducted comprehensive comparisons on MuJoCo benchmarks. The empirical results show that the proposed algorithms outperform recent diffusion-policy online RLs on most tasks, and the DPMD improves more than 120% over soft actor-critic on Humanoid and Ant. 

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2. CONSISTENCY MODELS AS A RICH AND EFFICIENT POLICY CLASS FOR REINFORCEMENT LEARNING

   Ding, Zihan; Jin, Chi (May 2024, International Conference on Learning Representations)

   Score-based generative models like the diffusion model have been testified to be effective in modeling multi-modal data from image generation to reinforcement learning (RL). However, the inference process of diffusion model can be slow, which hinders its usage in RL with iterative sampling. We propose to apply the consistency model as an efficient yet expressive policy representation, namely consistency policy, with an actor-critic style algorithm for three typical RL settings: offline, offline-to-online and online. For offline RL, we demonstrate the expressiveness of generative models as policies from multi-modal data. For offline-to-online RL, the consistency policy is shown to be more computational efficient than diffusion policy, with a comparable performance. For online RL, the consistency policy demonstrates significant speedup and even higher average performances than the diffusion policy. 

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3. The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation

   https://doi.org/10.1287/opre.2022.0357  

   Winnicki, Anna; Lubars, Joseph; Livesay, Michael; Srikant, R (May 2024, Operations Research)

   In many applications of reinforcement learning, the underlying system dynamics are known, but computing the optimal policy is still difficult because the size of the state space can be enormously large. For example, Shannon famously estimated the number of states in chess to be approximately 10^120. To handle such enormous state space sizes, policy iteration algorithms make two major approximations; it is assumed that the value function lies in a lower-dimensional space and that only a few steps of a trajectory (called rollout) are used to evaluate a policy. Using a counterexample, we show that approximations can lead to the divergence of policy iteration. We show that sufficient lookahead in the policy improvement step mitigates this divergence and leads to algorithms with bounded errors. We also show that these errors can be controlled by appropriately choosing an appropriate amount of lookahead and rollout. 

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4. Constraint-Conditioned Actor-Critic for Offline Safe Reinforcement Learning

   Guo, Zijian; Zhou, Weichao; Wang, Shengao; Li, Wenchao (March 2025, The Thirteenth International Conference on Learning Representations)

   Offline safe reinforcement learning (OSRL) aims to learn policies with high rewards while satisfying safety constraints solely from data collected offline. However, the learned policies often struggle to handle states and actions that are not present or out-of-distribution (OOD) from the offline dataset, which can result in violation of the safety constraints or overly conservative behaviors during their online deployment. Moreover, many existing methods are unable to learn policies that can adapt to varying constraint thresholds. To address these challenges, we propose constraint-conditioned actor-critic (CCAC), a novel OSRL method that models the relationship between state-action distributions and safety constraints, and leverages this relationship to regularize critics and policy learning. CCAC learns policies that can effectively handle OOD data and adapt to varying constraint thresholds. Empirical evaluations on the benchmarks show that CCAC significantly outperforms existing methods for learning adaptive, safe, and high-reward policies. 

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5. Offline Reinforcement Learning with Closed-Form Policy Improvement Operators

   Li, Jiachen and; Zhang, Edwin; Yin, Ming; Bai, Qinxun; Wang, Yu-Xiang; Wang, William Yang (July 2023, Proceedings of Machine Learning Research)

   Krause, Andreas and; Brunskill, Emma; Cho, Kyunghyun; Engelhardt, Barbara; Sabato, Sivan; Scarlett, Jonathan (Ed.)

   Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a policy is trained to maximize a learned value function while constrained by the behavior policy to avoid a significant distributional shift. In this paper, we propose our closed-form policy improvement operators. We make a novel observation that the behavior constraint naturally motivates the use of first-order Taylor approximation, leading to a linear approximation of the policy objective. Additionally, as practical datasets are usually collected by heterogeneous policies, we model the behavior policies as a Gaussian Mixture and overcome the induced optimization difficulties by leveraging the LogSumExp’s lower bound and Jensen’s Inequality, giving rise to a closed-form policy improvement operator. We instantiate both one-step and iterative offline RL algorithms with our novel policy improvement operators and empirically demonstrate their effectiveness over state-of-the-art algorithms on the standard D4RL benchmark. Our code is available at https://cfpi-icml23.github.io/. 

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