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This project explores the application of reinforcement learning (RL) to train humanoid robots for dynamic rock climbing movements, focusing on achieving the challenging "dyno" maneuver. Using the Proximal Policy Optimization (PPO) algorithm, the simulation integrates physics-based environments to model realistic climbing scenarios.