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Found 6 papers, 4 papers with code
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
Reinforcement learning (RL) often faces the challenges of uninformed search problems where the agent should explore without access to the domain knowledge such as characteristics of the environment or external rewards.
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
While reinforcement learning (RL) has achieved great success in acquiring complex skills solely from environmental interactions, it assumes that resets to the initial state are readily available at the end of each episode.
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation
Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed.
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training.
Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery
Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm.
Automating Reinforcement Learning with Example-based Resets
Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge.
