• 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
  • Thumbnail for Reinforcement learning from human feedback
    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
  • Thumbnail for 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...
    29 KB (3,011 words) - 15:06, 14 February 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
  • Thumbnail for Neural network (machine learning)
    Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning. Each corresponds...
    157 KB (17,036 words) - 21:07, 30 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
  • 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
  • Thumbnail for OpenAI
    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) - 19:34, 30 April 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
  • Supervised learning: Russell & Norvig (2021, §19.2) (Definition) Russell & Norvig (2021, Chpt. 19–20) (Techniques) Reinforcement learning: Russell & Norvig...
    216 KB (21,915 words) - 07:09, 29 April 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
  • Thumbnail for Quantum machine learning
    performance of reinforcement learning agents in the projective simulation framework. Reinforcement learning is a branch of machine learning distinct from...
    84 KB (10,195 words) - 18:09, 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
  • 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
  • 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
  • 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
  • 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
  • Thumbnail for ChatGPT
    conversational applications using a combination of supervised learning and reinforcement learning from human feedback. ChatGPT was released as a freely available...
    175 KB (15,246 words) - 01:53, 29 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
  • Thumbnail for Richard S. Sutton
    modern computational reinforcement learning, having several significant contributions to the field, including temporal difference learning and policy gradient...
    10 KB (861 words) - 05:20, 15 March 2024
  • Multi-objective reinforcement learning (MORL) is a form of reinforcement learning concerned with conflicting alternatives. It is distinct from multi-objective...
    879 bytes (91 words) - 10:41, 5 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
  • 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
  • contrast to traditional learning techniques which rely on supervised learning approaches that are less flexible, reinforcement learning recommendation techniques...
    86 KB (9,789 words) - 11:45, 27 April 2024
  • naturally produces gradient-based primal-dual algorithms in safe reinforcement learning. Adjustment of observations Duality Gittins index Karush–Kuhn–Tucker...
    50 KB (7,741 words) - 16:49, 20 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