Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take... 55 KB (6,582 words) - 12:51, 15 April 2024 |
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem... 27 KB (2,935 words) - 05:11, 23 March 2024 |
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. In classical... 43 KB (4,906 words) - 01:41, 29 April 2024 |
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the... 29 KB (3,785 words) - 06:23, 6 April 2024 |
In reinforcement learning (RL), a model-free algorithm (as opposed to a model-based one) is an algorithm which does not estimate the transition probability... 7 KB (656 words) - 09:02, 20 December 2023 |
signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize... 129 KB (14,257 words) - 19:02, 25 April 2024 |
absence of motor reproduction or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards... 49 KB (6,216 words) - 08:39, 29 April 2024 |
Softmax function (section Reinforcement learning) model which uses the softmax activation function. In the field of reinforcement learning, a softmax function can be used to convert values into action probabilities... 32 KB (4,929 words) - 12:36, 25 April 2024 |
OpenAI (section Reinforcement learning) OpenAI released a public beta of "OpenAI Gym", its platform for reinforcement learning research. Nvidia gifted its first DGX-1 supercomputer to OpenAI... 165 KB (14,070 words) - 03:28, 1 May 2024 |
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate... 12 KB (1,565 words) - 06:04, 27 April 2024 |
systems where there's no evident labeling or mapping of components. Reinforcement learning is employed to build models that progressively refine their system... 6 KB (574 words) - 17:33, 23 January 2024 |
Artificial intelligence (redirect from Probabilistic machine learning) Supervised learning: Russell & Norvig (2021, §19.2) (Definition) Russell & Norvig (2021, Chpt. 19–20) (Techniques) Reinforcement learning: Russell & Norvig... 216 KB (21,915 words) - 01:32, 1 May 2024 |
with reinforcement learning, such as learning a simplified version of a game first. Some domains have shown success with anti-curriculum learning: training... 13 KB (1,366 words) - 21:36, 27 April 2024 |
Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves... 11 KB (1,336 words) - 00:12, 22 July 2023 |
Operant conditioning (redirect from Operant learning) stimuli. The frequency or duration of the behavior may increase through reinforcement or decrease through punishment or extinction. Operant conditioning originated... 67 KB (8,836 words) - 14:12, 9 April 2024 |
systems without significant simplification and robustification. Reinforcement learning algorithms, in particular, require measuring their performance over... 9 KB (1,092 words) - 14:56, 19 December 2023 |
performance of reinforcement learning agents in the projective simulation framework. Reinforcement learning is a branch of machine learning distinct from... 85 KB (10,296 words) - 02:43, 1 May 2024 |
application of MDP process in machine learning theory is called learning automata. This is also one type of reinforcement learning if the environment is stochastic... 33 KB (4,869 words) - 23:58, 21 April 2024 |
professor at University College London. He has led research on reinforcement learning with AlphaGo, AlphaZero and co-lead on AlphaStar. He studied at... 8 KB (713 words) - 08:42, 3 January 2024 |
of fully self-contained autoencoder training. In reinforcement learning, self-supervising learning from a combination of losses can create abstract representations... 16 KB (1,770 words) - 20:12, 23 April 2024 |
Proximal policy optimization (category Reinforcement learning) Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult... 15 KB (2,082 words) - 21:28, 14 April 2024 |
model being used. Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned... 62 KB (7,161 words) - 15:16, 29 February 2024 |
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from... 52 KB (6,612 words) - 19:53, 17 April 2024 |
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or... 46 KB (6,385 words) - 17:04, 3 March 2024 |
Exploration-exploitation dilemma (category Machine learning) context of machine learning, the exploration-exploitation tradeoff is fundamental in reinforcement learning, a type of machine learning that involves training... 3 KB (309 words) - 03:47, 19 March 2024 |
Self-play (redirect from Self-play (reinforcement learning technique)) reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". In multi-agent reinforcement learning... 4 KB (498 words) - 21:09, 29 March 2024 |