Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
Explainable reinforcement learning in production control of job shop manufacturing system
A Kuhnle, MC May, L Schäfer… - International Journal of …, 2022 - Taylor & Francis
Manufacturing in the age of Industry 4.0 can be characterised by a high product variety and
complex material flows. The increasing individualisation of products requires adaptive …
complex material flows. The increasing individualisation of products requires adaptive …
Uncertainty-based offline reinforcement learning with diversified q-ensemble
Offline reinforcement learning (offline RL), which aims to find an optimal policy from a
previously collected static dataset, bears algorithmic difficulties due to function …
previously collected static dataset, bears algorithmic difficulties due to function …
Randomized ensembled double q-learning: Learning fast without a model
Exploration in deep reinforcement learning: From single-agent to multiagent domain
Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …
have achieved significant success across a wide range of domains, including game artificial …
Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation
Many Reinforcement Learning (RL) approaches use joint control signals (positions,
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
velocities, torques) as action space for continuous control tasks. We propose to lift the action …
Thompson sampling for improved exploration in gflownets
Generative flow networks (GFlowNets) are amortized variational inference algorithms that
treat sampling from a distribution over compositional objects as a sequential decision …
treat sampling from a distribution over compositional objects as a sequential decision …