3 papers
cs.LG2026
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
Faisal Mohamed, Catherine Ji, Benjamin Eysenbach +1
Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent perceives and represents the world. To lear…
cs.AI2025
ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning
Roger Creus Castanyer, Faisal Mohamed, Pablo Samuel Castro +2
Reinforcement learning (RL) algorithms are highly sensitive to reward function specification, which remains a central challenge limiting their broad applicability. We present ARM-F…
cs.LG2024
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed +1
Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised reinforcement learning (RL) have been shown to be effective in different environments, depend…