Reinforcement Learning
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
Honguk Woo is the corresponding author.
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
Reinforcement learning (RL) is a sequential decision-making problem in which an agent tries to maximize its expected cumulative reward by interacting with an unknown environment over time.
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultaneously influences many entities in the environment, making downstream skill chaining extremely challenging.
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
Existing solutions either work under very specific assumptions or achieve bounds that are vacuous in some regimes.