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Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain.
Jun 15, 2016
Abstract. Repeating the same action across multiple contiguous time-steps (“macro-actions”) in a reinforcement learn- ing setting speeds up the computation ...
Deep reinforcement learning with macro-actions. Published in arXiv preprint arXiv:1606.04615, 2016. Recommended citation: Ishan P Durugkar, ...
Conventional deep reinforcement learning typically determines an appro- priate primitive action at each timestep, which requires enormous amount.
Jun 15, 2016 · In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliabil- ity of deep ...
We consider the challenges of learning multi-agent/robot macro-action-based deep Q-nets including how to properly update each macro-action value and accurately ...
Nov 24, 2022 · In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning ( ...
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This pa- per proposes two Deep Q-Network (DQN) based methods for learning decentral- ized and centralized macro-action-value functions with novel macro-action ...
An implementation of five reinforcement learning algorithms to simulate macro actions for the HFO problem. - martiansideofthemoon/macro-action-rl.
Jun 15, 2016 · A novel multi-agent reinforcement learning approach is proposed to learn the coordinated behaviors among cooperative agents team. The proposed ...