Name
Abstract | ||
|---|---|---|
This paper deals with distributed reinforcement learning (DRL), which involves a central controller and a group of learners. In particular, two DRL settings encountered in several applications are considered: multi-agent reinforcement learning (RL) and parallel RL, where frequent information exchanges between the learners and the controller are required. For many practical distributed systems, however, such as those involving parallel machines for training deep RL algorithms, and multi-robot systems for learning the optimal coordination strategies, the overhead caused by these frequent communication exchanges is considerable, and becomes the bottleneck of the overall performance. To address this challenge, a novel policy gradient method is developed here to cope with such communication-constrained DRL settings. The proposed approach reduces the communication overhead without degrading learning performance by adaptively skipping the policy gradient communication during iterations. It is established analytically that i) the novel algorithm has convergence rate identical to that of the plain-vanilla policy gradient for DRL; while ii) if the distributed computing units are heterogeneous in terms of their reward functions and initial state distributions, the number of communication rounds needed to achieve a desirable learning accuracy is markedly reduced. Numerical experiments on a popular multi-agent RL benchmark corroborate the significant communication reduction attained by the novel algorithm compared to alternatives. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1812.03239 | 0 | 0.34 |
References | Authors | |
25 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyi Chen | 1 | 94 | 14.50 |
| Kaiqing Zhang | 2 | 48 | 13.02 |
| G. B. Giannakis | 3 | 11464 | 1206.47 |
| Tamer Basar | 4 | 3497 | 402.11 |