3 papers
cs.AI2026
Learning Multiple Timescales for Goal-Conditioned Reinforcement Learning
Pedro Robles Dutenhefner, Dikshant Shehmar, Wagner Meira +1
Existing approaches to offline goal-conditioned reinforcement learning (GCRL) struggle with long-horizon tasks. Discounting shrinks value differences between distant states until t…
cs.LG2026
Laplacian Representations for Decision-Time Planning
Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor +1
Planning with a learned model remains a key challenge in model-based reinforcement learning (RL). In decision-time planning, state representations are critical as they must support…
cs.RO2025
An Adaptive Transition Framework for Game-Theoretic Based Takeover
Dikshant Shehmar, Matthew E. Taylor, Ehsan Hashemi
The transition of control from autonomous systems to human drivers is critical in automated driving systems, particularly due to the out-of-the-loop (OOTL) circumstances that reduc…