Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Off-policy reinforcement learning (RL) offers data efficiency through sample reuse and safe environment interaction.
• Existing off-policy policy gradient methods struggle with high bias or variance, leading to unreliable estimates.
• Real-world applications like robot learning are hindered by the high sample cost and inefficiency of current RL methods.

Purpose of the Study:

• To develop a more data-efficient and reliable off-policy reinforcement learning method.
• To address the limitations of high bias and high variance in current policy gradient techniques.
• To provide a differentiable policy gradient estimate without relying on importance sampling or semi-gradient methods.

Main Methods:

• Proposed a nonparametric Bellman equation solvable in closed form.
• The solution provides a differentiable policy gradient estimate.
• Empirically analyzed the gradient estimate quality against state-of-the-art methods.

Main Results:

• The proposed method avoids the high variance of importance sampling.
• The proposed method avoids the high bias of semi-gradient methods.
• Empirical analysis showed superior sample efficiency on classical control tasks compared to baselines.

Conclusions:

• The nonparametric Bellman equation offers a promising solution for improving off-policy RL.
• This approach enhances data efficiency and gradient estimation reliability.
• The method has potential for broader application in interaction-driven learning scenarios.