<ul><li>MuJoCo environments are essential for studying balance, energy efficiency, and real-world applicability of AI control policies.</li><li>To work with MuJoCo and OpenAI Gym, set up the environment by following specific steps and design locomotion tasks using functions like reset(), step(action), and render().</li><li>Popular reinforcement learning algorithms like PPO, SAC, and TD3 can be integrated into MuJoCo environments using libraries like Stable-Baselines3.</li><li>Challenges in humanoid locomotion include high degrees of freedom and balance requirements, emphasizing the importance of tuning reward functions for optimal results.</li></ul>

Mastering Locomotion in MuJoCo: A Practical Guide for Robotics and Reinforcement Learning…

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