Feb. 13, 2024, 5:41 a.m. | Simone Parisi Montaser Mohammedalamen Alireza Kazemipour Matthew E. Taylor Michael Bowling

cs.LG updates on arXiv.org arxiv.org

In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assumption that rewards are always observable is often not applicable in real-world problems. For example, the agent may need to ask a human to supervise its actions or activate a monitoring system to receive feedback. There may even be a period of time before rewards become observable, or a period of time after …

agent cs.lg decision environment example feedback human markov numerical observable processes reinforcement reinforcement learning world

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