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This chapter explores reinforcement learning (RL) as a method for enabling robots to autonomously collect data and refine their skills through interaction with their environment. It covers key RL concepts, including Markov Decision Processes (MDP), model-free and model-based approaches, and techniques like RLHF and DPO for aligning models with human preferences. The chapter also discusses challenges such as data scarcity and reward design, highlighting future directions in sample efficiency, transfer learning, and sim-to-real adaptation for improving RL in robotics.

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Reinforcement Learning and Control

  • 章节

  • 发表日期：

    2025年

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  • Alishba Imran,
  • Keerthana Gopalakrishnan

摘要

This chapter explores reinforcement learning (RL) as a method for enabling robots to autonomously collect data and refine their skills through interaction with their environment. It covers key RL concepts, including Markov Decision Processes (MDP), model-free and model-based approaches, and techniques like RLHF and DPO for aligning models with human preferences. The chapter also discusses challenges such as data scarcity and reward design, highlighting future directions in sample efficiency, transfer learning, and sim-to-real adaptation for improving RL in robotics.