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Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning.

. NeurIPS, (2022)

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Zhang Zhang

Meng Zhang

Methods and implementations of road-network matching. TU München, (2009)

Le Zhang

Yue Zhang

Xianghui Zhang

 

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Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning., , , и . CoLLAs, том 232 из Proceedings of Machine Learning Research, стр. 292-317. PMLR, (2023)Adaptive-Gradient Policy Optimization: Enhancing Policy Learning in Non-Smooth Differentiable Simulations., , , , и . ICML, OpenReview.net, (2024)Self-Exploring Language Models: Active Preference Elicitation for Online Alignment., , , , , , и . CoRR, (2024)Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics., , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 41219-41243. PMLR, (2023)Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning.. NeurIPS, (2022)One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration., , , , , , , , и . CoRR, (2023)Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms., , , и . CoRR, (2023)Asking Before Action: Gather Information in Embodied Decision Making with Language Models., , , , и . CoRR, (2023)Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration., , , , , , , , и . NeurIPS, (2023)Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents., , , , , , и . ICML, OpenReview.net, (2024)