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1. Reinforcement learning: Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms ... (Field of machine learning) [100%] 2023-12-01 [Reinforcement learning] [Markov models]...
2. Reinforcement learning: Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three ... (Field of machine learning) [100%] 2023-12-11 [Reinforcement learning] [Markov models]...
3. Reinforcement learning: Reinforcement learning (RL) is learning by interacting with an environment. An RL agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on basis of its past experiences (exploitation) and also ... [100%] 2021-12-24 [Conditioning] [Reinforcement Learning]...
4. Reinforcement learning from human feedback: In machine learning, reinforcement learning from human feedback (RLHF), including reinforcement learning from human preferences, is a technique that trains a "reward model" directly from human feedback and uses the model as a reward function to optimize an agent's ... (Machine learning technique) [63%] 2023-12-20 [Machine learning] [Reinforcement learning]...
5. Reinforcement learning from human feedback: In machine learning, reinforcement learning from human feedback (RLHF), including reinforcement learning from human preferences, is a technique that trains a "reward model" directly from human feedback and uses the model as a reward function to optimize an agent's ... (Machine learning technique) [63%] 2024-07-24 [Machine learning] [Reinforcement learning]...
6. Reinforcement Learning: Reinforcement learning (RL) is the process of optimizing rewards in a sequential decision making process under uncertainty. Creating a reinforcement learning algorithm is specifically challenging because of the following items: Imitation learning reduces RL to supervised learning by learning from ... [100%] 2023-12-11 [Reinforcement learning]
7. Deep reinforcement learning: Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. (Machine learning that combines deep learning and reinforcement learning) [81%] 2023-12-10 [Machine learning algorithms] [Reinforcement learning]...
8. Multi-agent reinforcement learning: Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. (Sub-field of reinforcement learning) [70%] 2023-12-02 [Reinforcement learning] [Multi-agent systems]...
9. Self-play (reinforcement learning technique): Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". (Reinforcement learning technique) [63%] 2023-05-12 [Reinforcement learning] [Machine learning algorithms]...