There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially ob...
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
Automatically building maps from sensor data is a necessary and fundamental skill for mobile robots; as a result, considerable research attention has focused on the technical chall...
Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stationary environments is of course a desired feature of a fair RL algorithm. Yet, even if the...
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-a...
Rajbala Makar, Sridhar Mahadevan, Mohammad Ghavamz...