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A parallel reinforcement computing model for function optimization problems. Abstract: Learning Automaton is a learning model with outstanding learning ...
The feasibility of the proposed approach is validated by simulation studies using a multi-agent simulator on an FPGA (Field- Programmable Gate Array) and ...
A parallel reinforcement computing model for function optimization problems. Qian Fei, Shigeya Ikebou, Takashi Kusunoki, Jijun Wu, Hironori Hirata.
Feb 2, 2024 · Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster ...
Sep 15, 2023 · We model the problem as a Markov decision process (MDP) and develop a proximal policy optimization (PPO) algorithm, a DRL technique. Notably, ...
Abstract. This paper presents an open-source, parallel AI environment. (named OpenGraphGym) to facilitate the application of reinforcement.
A technique is presented that is suitable for function optimization in high-dimensional binary domains. The method allows an efficient parallel.
This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to ...
This paper investigates the use of parallelization in reinforcement learning (RL) with the goal of learning optimal policies for single-agent RL problems ...
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL ...