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sample efficiency

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Co-occurring keywords · papers mentioning both

reinforcement learning (179) deep reinforcement learning (86) model-based reinforcement learning (79) continuous control (47) policy optimization (44) representation learning (43) policy gradient (37) imitation learning (29) value function (29) off-policy learning (29)

Papers

Equivariant Diffusion Policy CoRL 2024 Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning ICLR 2024 Exploring Beyond Curiosity Rewards: Language-Driven Exploration in RL ACML 2024 Exploring the Learning Capabilities of Language Models using LEVERWORLDS EMNLP 2024 EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data CoRL 2024 Feasible Reachable Policy Iteration ICML 2024 Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D ICLR 2024 Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization NeurIPS 2024 Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search NeurIPS 2024 Genetic-guided GFlowNets for Sample Efficient Molecular Optimization NeurIPS 2024 Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction ICML 2024 How Does Goal Relabeling Improve Sample Efficiency? ICML 2024 How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach NeurIPS 2024 How to Learn Domain-Invariant Representations for Visual Reinforcement Learning: An Information-Theoretical Perspective IJCAI 2024 Hybrid CtrlFormer: Learning Adaptive Search Space Partition for Hybrid Action Control via Transformer-based Monte Carlo Tree Search UAI 2024 Hybrid Inverse Reinforcement Learning ICML 2024 IMAGINATION POLICY: Using Generative Point Cloud Models for Learning Manipulation Policies CoRL 2024 Imitation Bootstrapped Reinforcement Learning RSS 2024 Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures ICLR 2024 Learning Causal Dynamics Models in Object-Oriented Environments ICML 2024 Learning Generalizable and Composable Abstractions for Transfer in Reinforcement Learning AAAI 2024 Leaving the Nest: Going beyond Local Loss Functions for Predict-Then-Optimize AAAI 2024 Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation CoRL 2024 Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning AAAI 2024 Limits of Transformer Language Models on Learning to Compose Algorithms NeurIPS 2024