The UCL Deciding, Acting, and Reasoning with Knowledge (DARK) Lab is a Reinforcement Learning research group at the UCL Centre for Artificial Intelligence. We focus on research in complex open-ended environments that provide a constant stream of novel observations without reliable reward functions, often requiring agents to create their own curricula and to deal with external knowledge, natural language, and hard exploration problems.
News
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19/02/2024:
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control has been accepted to ICRA 2024 (oral).
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19/02/2024:
H-GAP: Humanoid Control with a Generalist Planner has been accepted to ICLR 2024 (spotlight).
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19/02/2024:
Understanding the Effects of RLHF on LLM Generalisation and Diversity, Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks and Reward Model Ensembles Help Mitigate Overoptimization have been accepted to ICLR 2024.
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18/02/2024:
Multi-Agent Diagnostics for Robustness via Illuminated Diversity has been accepted to AAMAS 2024 (oral).
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25/09/2023:
Large language models are not zero-shot communicators has been accepted to NeurIPS 2023 (spotlight).
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25/09/2023:
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning has been accepted to NeurIPS 2023.
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24/01/2023:
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning has been accepted to ICLR 2023.
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24/01/2023:
Efficient Planning in a Compact Latent Action Space has been accepted to ICLR 2023.