META-Learning Eligibility Traces for More Sample Efficient Temporal Difference Learning
This addresses a specific bottleneck in reinforcement learning for practitioners, offering a more sample-efficient approach, though it is incremental as it builds on existing TD-learning frameworks.
The paper tackles the problem of tuning the eligibility trace parameter in Temporal-Difference learning, which is time-consuming and can lead to inefficient learning, by proposing a meta-learning method that adjusts this parameter in a state-dependent manner, resulting in significant performance improvements and improved robustness to learning rate variation.
Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well as algorithms which learn how to improve policies. TD-learning with eligibility traces provides a way to do temporal credit assignment, i.e. decide which portion of a reward should be assigned to predecessor states that occurred at different previous times, controlled by a parameter $λ$. However, tuning this parameter can be time-consuming, and not tuning it can lead to inefficient learning. To improve the sample efficiency of TD-learning, we propose a meta-learning method for adjusting the eligibility trace parameter, in a state-dependent manner. The adaptation is achieved with the help of auxiliary learners that learn distributional information about the update targets online, incurring roughly the same computational complexity per step as the usual value learner. Our approach can be used both in on-policy and off-policy learning. We prove that, under some assumptions, the proposed method improves the overall quality of the update targets, by minimizing the overall target error. This method can be viewed as a plugin which can also be used to assist prediction with function approximation by meta-learning feature (observation)-based $λ$ online, or even in the control case to assist policy improvement. Our empirical evaluation demonstrates significant performance improvements, as well as improved robustness of the proposed algorithm to learning rate variation.