QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
arXiv preprint arXiv:2202.04478 , year=
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Occupancy Reward Shaping extracts goal-reaching rewards from world-model occupancy measures using optimal transport, improving offline goal-conditioned RL performance 2.2x on 13 tasks without changing the optimal policy.
Analysis reveals Pi-GCRL degradation in contact-rich tasks due to hybrid dynamics; contact-aware and hierarchical formulations are proposed to extend it to manipulation.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Occupancy Reward Shaping extracts goal-reaching rewards from world-model occupancy measures using optimal transport, improving offline goal-conditioned RL performance 2.2x on 13 tasks without changing the optimal policy.
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Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
Analysis reveals Pi-GCRL degradation in contact-rich tasks due to hybrid dynamics; contact-aware and hierarchical formulations are proposed to extend it to manipulation.