Reinforcement Learning
Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice
AI systems need exact descriptions of tasks to be performed.
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In the classical decision-making literature, this is achieved by two interweaving processes, policyevaluation and policyimprovement (Sutton and Barto,2018).
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards
MetaCURL: Non-stationary Concave Utility Reinforcement Learning Bianca Marin Moreno Inria
We explore online learning in episodic Markov decision processes on non-stationary environments (changing losses and probability transitions).
Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization
Multi-task offline reinforcement learning aims to develop a unified policy for diverse tasks without requiring real-time interaction with the environment.
Diffuser Lite: Towards Real-time Diffusion Planning Zibin Dong 1 Jianye Hao
The capability of generating high-quality long-horizon trajectories makes it a promising research direction.
Robot Policy Learning with Temporal Optimal Transport Reward
Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice.
