Spectral Representation-based Reinforcement Learning

Chenxiao Gao, Haotian Sun, Na Li, Dale Schuurmans, Bo Dai

Code | Project

• We derive spectral representations from a decomposition of the transition operator, yielding an effective abstraction of the system dynamics with a clear theoretical characterization for downstream policy optimization.
• The framework covers latent-variable and energy-based dynamics, extends to POMDPs, and matches or exceeds strong baselines on 20+ DeepMind Control Suite tasks.

Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning

Chenxiao Gao, Chenyang Wu, Mingjun Cao, Chenjun Xiao, Yang Yu, Zongzhang Zhang

Code | Project Page

• We Introduce pathwise KL to estimate and control the divergences between two diffusion policies;
• Leveraging pathwise KL, we propose an actor-critic framework with two-time-scale temporal difference learning to efficiently optimize diffusion policies with behavior regularization.

Reinforced In-Context Black-Box Optimization

Lei Song*, Chenxiao Gao*, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao, Zongzhang Zhang, Chao Qian

Code

• RIBBO distills and reinforces existing black-box optimization algorithms by fitting regret augmented learning histories of the behavior algorithms.
• By specifying a suitable regret-to-go, RIBBO generate better query decisions by auto-regressively predicting the next points.

Diffusion Spectral Representation for Reinforcement Learning

Chenxiao Gao*, Dmitry Shribak*, Yitong Li, Chenjun Xiao, Bo Dai

Code | Project

• We leverages the flexibility of diffusion models and extract spectral representations (Diff-SR) that capture the dynamics structure.
• Diff-SR is able to represent the value function of any policy sufficiently, paving the way for efficient planning and exploration for downstream RL optimization.

Hindsight Preference Learning for Offline Preference-based Reinforcement Learning

Chenxiao Gao, Shengjun Fang, Chenjun Xiao, Yang Yu, Zongzhang Zhang

Code

• We identified shortages of the widely used preference modeling method in existing PbRL settings.
• HPL leverages the vast unlabeled dataset to facilitate credit assignment, providing robust and advantageous rewards for downsteam RL optimization.

Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization

Rui Kong, Chenyang Wu, Chenxiao Gao, Yang Yu, Zongzhang Zhang

Code

• We identify two pain points in offline-to-online reinforcement learning: 1) value overestimation causes fluctuations during learning, and 2) the primacy bias hinders the policy from further improvement.
• With the proposed Continual Policy Revitalization, we can fine-tune pret-rained policies efficiently and stably.

Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning

Chengxing Jia*, Chenxiao Gao*, Hao Yin, Fuxiang Zhang, Xiong-Hui Chen, Tian Xu, Lei Yuan, Zongzhang Zhang, Yang Yu, Zhi-Hua Zhou

• We explore the idea of rehearsal for offline reinforcement learning, which generates diverse while eligible dynamics models using extremely limited amount of data and optimizes an contextual policy with the generated models.
• By recognizing the context, the policy is able to generalize to the environment at hand during the online stage.

Disentangling Policy from Offline Task Rpresentation Learning via Adversarial Data Augmentation

Chengxing Jia, Fuxiang Zhang, Yi-Chen Li, Chenxiao Gao, Xu-Hui Liu, Lei Yuan, Zongzhang Zhang, Yang Yu.

Code

• Learned task representations from previous OMRL methods tend to correlate spuriously with the behavior policy instead of the task.
• We disentangle the effect of behavior policies from representation learning by adversarial data augmentation.

ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning

Chenxiao Gao, Chenyang Wu, Mingjun Cao, Rui Kong, Zongzhang Zhang, Yang Yu

Code

• We identify failure modes of existing return-conditioned decision-making systems, and suggest to use advantages as the property token for conditional generation.

Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations

Renzhe Zhou, Chenxiao Gao, Zongzhang Zhang, Yang Yu

Code

• Real-world RL applications are with data limitations, such as limited tasks and limited behavior diversity.
• We develop GENTLE, a simple yet effective task representation learning method to extract generalizable and accurate task representations from offline contextual datasets.