META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

Understand

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 boost sample efficiency by temporal credit assignment, i.e.
• deciding which portion of a reward should be assigned to predecessor states that occurred at different previous times, controlled by a parameter $\lambda$.
• However, tuning this parameter can be time-consuming, and not tuning it can lead to inefficient learning.