Back to Search
Start Over
Double Q($\sigma$) and Q($\sigma, \lambda$): Unifying Reinforcement Learning Control Algorithms
- Publication Year :
- 2017
-
Abstract
- Temporal-difference (TD) learning is an important field in reinforcement learning. Sarsa and Q-Learning are among the most used TD algorithms. The Q($\sigma$) algorithm (Sutton and Barto (2017)) unifies both. This paper extends the Q($\sigma$) algorithm to an online multi-step algorithm Q($\sigma, \lambda$) using eligibility traces and introduces Double Q($\sigma$) as the extension of Q($\sigma$) to double learning. Experiments suggest that the new Q($\sigma, \lambda$) algorithm can outperform the classical TD control methods Sarsa($\lambda$), Q($\lambda$) and Q($\sigma$).
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1711.01569
- Document Type :
- Working Paper