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Found 19 papers, 9 papers with code
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions.
Regularized Conditional Diffusion Model for Multi-Task Preference Alignment
Sequential decision-making is desired to align with human intents and exhibit versatility across various tasks.
Large-Scale Actionless Video Pre-Training via Discrete Diffusion for Efficient Policy Learning
In the fine-tuning stage, we harness the imagined future videos to guide low-level action learning trained on a limited set of robot data.
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments
In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty.
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
Experimental results illustrate the superiority of RO2O in facilitating stable offline-to-online learning and achieving significant improvement with limited online interactions.
Privileged Knowledge Distillation for Sim-to-Real Policy Generalization
In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap.
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings.
On the Value of Myopic Behavior in Policy Reuse
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
Behavior Contrastive Learning for Unsupervised Skill Discovery
Under mild assumptions, our objective maximizes the MI between different behaviors based on the same skill, which serves as an upper bound of the previous MI objective.
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
Offline reinforcement learning (RL) provides a promising direction to exploit massive amount of offline data for complex decision-making tasks.
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL.
False Correlation Reduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems.
Dynamic Bottleneck for Robust Self-Supervised Exploration
Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards.
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning
Many exploration strategies are built upon the optimism in the face of the uncertainty (OFU) principle for reinforcement learning.
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks.
Principled Exploration via Optimistic Bootstrapping and Backward Induction
In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I).
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning
However, such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning (DRL).
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded.
