Reinforcement Learning in Robotics: Policy Optimization Hacks

Reinforcement Learning (RL) trains agents via reward signals. Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) are popular for continuous control tasks.

Key Uses:

Robotic manipulation, drone navigation, and quadruped locomotion.

Hacks:

Use domain randomization during simulation training for better sim-to-real transfer. Apply Hindsight Experience Replay (HER) for sparse reward problems. Curriculum learning to gradually increase task difficulty. Combine with imitation learning (behaviour cloning) for sample efficiency.

References:

Schulman, J., et al. (2017). Proximal Policy Optimization Algorithms. arXiv:1707.06347.

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