Algorithms

Bidirectional-Reachable Hierarchical Reinforcement Learning with Mutually Responsive Policies
Hierarchical reinforcement learning (HRL) addresses complex long-horizon tasks by skillfully decomposing them into subgoals. Therefore, …
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Instruction-Guided Visual Masking
Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment …
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Instruction-Guided Visual Masking
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a …
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Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
Off-policy reinforcement learning (RL) has achieved notable success in tackling many complex real-world tasks, by leveraging previously …
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DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Multimodal pretraining has emerged as an effective strategy for the trinity of goals of representation learning in autonomous robots: …
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Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
Learning high-quality Q-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) …
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A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data. However, …
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A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Query-Policy Misalignment in Preference-Based Reinforcement Learning
Preference-based reinforcement learning (PbRL) provides a natural way to align RL agents’ behavior with human desired outcomes, but is …
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Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
In this study, we investigate the DIstribution Correction Estimation (DICE) methods, an important line of work in offline reinforcement …
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Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Safe offline reinforcement learning is a promising way to bypass risky online interactions towards safe policy learning. Most existing …
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Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model