Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the

Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that

learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training

Official code for "Can Wikipedia Help Offline Reinforcement Learning?" by Machel Reid, Yutaro Yamada and Shixiang Shane Gu (⭐ 106)

Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient

Learning in Continuous State and Action Spaces". In Wiering, Marco; Otterlo, Martijn van (eds.). Reinforcement Learning: State-of-the-Art. Springer Science

Shane; Hassabis, Demis (25 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518

research scientist at Google DeepMind and a professor at University College London. He has led research on reinforcement learning with AlphaGo, AlphaZero and

pleasure, and positive reinforcement), and reinforcement learning (e.g., Pavlovian-instrumental transfer); hence, it has a significant role in addiction

synthesis and optimization reinforcement learning is used to perform logic optimization directly. In some cases agents are trained to choose a series

telecommunications and reinforcement learning. Reinforcement learning utilizes the MDP framework to model the interaction between a learning agent and its environment

MacAlpine, Patrick (February 2022). "Outracing champion Gran Turismo drivers with deep reinforcement learning". Nature. 602 (7896): 223–228. Bibcode:2022Natur

that appeared on the cover of Nature entitled Outracing champion Gran Turismo drivers with deep reinforcement learning, which reported on the creation

Skill chaining is a skill discovery method in continuous reinforcement learning. It has been extended to high-dimensional continuous domains by the related

"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning". arXiv:2501.12948 [cs.CL]. DeepSeek 支持"深度思考+联网检索"能力 [DeepSeek adds

provisioning for adaptive multimedia in mobile communication networks by reinforcement learning". Mobile Networks and Applications. 11 (1): 101–110. CiteSeerX 10