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Showing 276–300 of 677 papers

TitleStatusHype
An Invariant Information Geometric Method for High-Dimensional Online OptimizationCode0
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction—0
Adaptive trajectory-constrained exploration strategy for deep reinforcement learningCode0
DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects—0
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments—0
GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction EstimationCode0
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation—0
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning—0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning—0
Supported Trust Region Optimization for Offline Reinforcement Learning—0
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
An Intelligent Social Learning-based Optimization Strategy for Black-box Robotic Control with Reinforcement Learning—0
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula—0
A Tractable Inference Perspective of Offline RL—0
Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations—0
Mind the Model, Not the Agent: The Primacy Bias in Model-based RL—0
Policy Gradient with Kernel Quadrature—0
One is More: Diverse Perspectives within a Single Network for Efficient DRL—0
Benchmarking the Sim-to-Real Gap in Cloth Manipulation—0
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios—0
Imitation Learning from Purified DemonstrationsCode0
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates—0
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
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