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Found 5 papers, 3 papers with code
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
To seamlessly integrate both modalities, we introduce a two-level hierarchical framework, RL-GPT, comprising a slow agent and a fast agent.
Creative Agents: Empowering Agents with Imagination for Creative Tasks
We benchmark creative tasks with the challenging open-world game Minecraft, where the agents are asked to create diverse buildings given free-form language instructions.
Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks
Our method outperforms baselines by a large margin and is the most sample-efficient demonstration-free RL method to solve Minecraft Tech Tree tasks.
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
We study offline meta-reinforcement learning, a practical reinforcement learning paradigm that learns from offline data to adapt to new tasks.
DLGAN: Disentangling Label-Specific Fine-Grained Features for Image Manipulation
By mapping the discrete label-specific attribute features into a continuous prior distribution, we leverage the advantages of both discrete labels and reference images to achieve image manipulation in a hybrid fashion.
