LGMay 6, 2016
Some Simulation Results for Emphatic Temporal-Difference Learning Algorithms
arXiv:1605.02099v12 citations
Originality Synthesis-oriented
AI Analysis
This work provides empirical validation for reinforcement learning algorithms, but it is incremental as it builds on existing theoretical studies.
The paper presents simulation results for emphatic temporal-difference learning algorithms, illustrating their behavior on three example problems to supplement prior theoretical analysis.
This is a companion note to our recent study of the weak convergence properties of constrained emphatic temporal-difference learning (ETD) algorithms from a theoretic perspective. It supplements the latter analysis with simulation results and illustrates the behavior of some of the ETD algorithms using three example problems.