Reproduce “Solving Robotics Tasks with Prior Demonstration via Exploration-Efficient Deep Reinforcement Learning” on OpenResearch
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About this paper
Chengyandan Shen, Christoffer Sloth
An exploration-efficient Deep Reinforcement Learning framework, DRLR, integrates prior demonstrations to enhance learning for robotics tasks by mitigating bootstrapping errors and state distribution shifts. The framework achieved a 347% increase in averaged reward for a high-dimensional sparse-reward task and demonstrated successful real-world deployment on an industrial wheel loader.
Key insights
- Calibrating Q-value comparisons for the reference policy by sampling from expert demonstrations significantly reduces bootstrapping errors and mitigates state distribution shifts.
- Employing a maximum entropy RL algorithm like SAC improves exploration efficiency and robustness against overfitting, leading to better performance in complex and sparse-reward scenarios.
- Using an offline DRL method (e.g., TD3+BC) as the reference policy provides greater robustness against suboptimal or noisy expert demonstrations compared to simple behavior cloning.
Summary by alphaXiv.
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