Adaptive Temporal Difference Learning With Linear Function Approximation.

Area of Science:

• Reinforcement Learning
• Machine Learning Theory
• Artificial Intelligence

Background:

• Temporal Difference (TD) learning is crucial for policy evaluation in reinforcement learning.
• Standard TD(0) and TD(λ) algorithms exhibit sensitivity to stepsize parameters, often leading to slow convergence.
• A strong connection exists between TD(0) and stochastic gradient methods.

Purpose of the Study:

• To develop a more robust and efficient adaptive variant of the TD(0) algorithm.
• To introduce an adaptive version of the TD(λ) algorithm.
• To analyze the theoretical convergence properties and practical performance of the proposed adaptive algorithms.

Main Methods:

• Introduced AdaTD(0), an adaptive projected variant of TD(0) with linear function approximation.
• Established theoretical convergence guarantees for AdaTD(0), including iteration complexity analysis.
• Developed AdaTD(λ) as an adaptive extension of TD(λ).
• Conducted empirical evaluations on standard reinforcement learning tasks.

Main Results:

• AdaTD(0) demonstrates robustness to stepsize selection, unlike traditional TD(0).
• The iteration complexity of AdaTD(0) is comparable to TD(0) in the general case, with potential for acceleration in sparse settings.
• AdaTD(λ) is proposed as an adaptive alternative to TD(λ).
• Empirical results validate the effectiveness of both AdaTD(0) and AdaTD(λ).

Conclusions:

• The proposed adaptive TD algorithms, AdaTD(0) and AdaTD(λ), offer significant improvements in robustness and convergence for policy evaluation.
• These adaptive methods address key limitations of existing TD learning algorithms, paving the way for more reliable reinforcement learning applications.