Adaptive Reinforcement Learning for Unobservable Random Delays

arXiv:2506. 14411v2 Announce Type: replace-cross Abstract: In standard reinforcement learning (RL) settings, the interaction between the agent and the environment is typically modeled as a Markov decision process (MDP), which assumes that the agent observes the system state instantaneously, selects an action without delay, and executes it immediately.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

• agents
• reinforcement-learning
• robotics
• benchmarks