Episodic Reinforcement Learning
Episodic reinforcement learning (ERL) focuses on learning optimal policies within finite-length episodes, aiming to maximize cumulative rewards over a defined horizon. Current research emphasizes improving sample efficiency and addressing challenges like sparse rewards, model uncertainty, and multi-agent interactions, often employing model-free and model-based algorithms, including variations of Q-learning, actor-critic methods, and Monte Carlo Tree Search. These advancements are significant for real-world applications such as robotics and autonomous systems, where efficient learning from limited interactions and adaptation to unforeseen circumstances are crucial.
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