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A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving
decision-making tasks in various domains, such as robotics, transportation, recommender …
decision-making tasks in various domains, such as robotics, transportation, recommender …
DPM: Dual Preferences-based Multi-Agent Reinforcement Learning
Preference-based Reinforcement Learning (PbRL), which optimizes reward functions using
preference feedback, is a promising approach for environments where handcrafted reward …
preference feedback, is a promising approach for environments where handcrafted reward …
VLP: Vision-Language Preference Learning for Embodied Manipulation
Reward engineering is one of the key challenges in Reinforcement Learning (RL).
Preference-based RL effectively addresses this issue by learning from human feedback …
Preference-based RL effectively addresses this issue by learning from human feedback …
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision
Retrieval-augmented generation (RAG) has shown great potential for knowledge-intensive
tasks, but its traditional architectures rely on static retrieval, limiting their effectiveness for …
tasks, but its traditional architectures rely on static retrieval, limiting their effectiveness for …
Multilinguality in LLM-Designed Reward Functions for Restless Bandits: Effects on Task Performance and Fairness
Restless Multi-Armed Bandits (RMABs) have been successfully applied to resource
allocation problems in a variety of settings, including public health. With the rapid …
allocation problems in a variety of settings, including public health. With the rapid …