Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes

1. 1Apply emphatic temporal-difference learning to stabilize off-policy TD updates
2. 2Analyze the mean map contraction of the ETD algorithm
3. 3Use regenerative-cycle analysis to separate the sign of the top Lyapunov exponent from the infinite variance of the follow-on trace
4. 4Implement constant-stepsize sampled dynamics in reinforcement learning algorithms
5. 5Evaluate the stability of ETD under constant stepsizes using Lyapunov exponents

Researchers and engineers working on reinforcement learning and temporal-difference learning will benefit from this article, as it provides new insights into the stability of ETD under constant stepsizes

💡 ETD stabilizes off-policy TD updates, but its stability under constant stepsizes depends on the contraction of the mean map and the sign of the top Lyapunov exponent

🤖 New research on Regularized Emphatic Temporal-Difference Learning: stability under constant stepsizes in #reinforcementlearning