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cs.LG2024
Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments
Runfa Chen, Ling Wang, Yu Du +4
Learning policies for multi-entity systems in 3D environments is far more complicated against single-entity scenarios, due to the exponential expansion of the global state space as…
cs.LG2024
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
Yu Luo, Tianying Ji, Fuchun Sun +3
Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge. Existing works often overl…
cs.LG2024
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
Yu Luo, Tianying Ji, Fuchun Sun +3
Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously collected data for policy learning. However,…