signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Q-learning at...

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

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

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

signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize...

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...

In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward...

Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning. Each corresponds...

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...

processing, computer vision (vision transformers), reinforcement learning, audio, multimodal learning, robotics, and even playing chess. It has also led...

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

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...

OpenAI released a public beta of "OpenAI Gym", its platform for reinforcement learning research. Nvidia gifted its first DGX-1 supercomputer to OpenAI...

Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations....

absence of motor reproduction or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards...

systems without significant simplification and robustification. Reinforcement learning algorithms, in particular, require measuring their performance over...

with reinforcement learning, such as learning a simplified version of a game first. Some domains have shown success with anti-curriculum learning: training...

stimuli. The frequency or duration of the behavior may increase through reinforcement or decrease through punishment or extinction. Operant conditioning originated...

extended this approach to optimization in 2017. In the 1990s, Meta Reinforcement Learning or Meta RL was achieved in Schmidhuber's research group through...

one for losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search,...

computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems...

Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent's decision function to accomplish difficult...

In behavioral psychology, reinforcement refers to consequences that increase the likelihood of an organism's future behavior, typically in the presence...

performance of reinforcement learning agents in the projective simulation framework. Reinforcement learning is a branch of machine learning distinct from...

modern computational reinforcement learning, having several significant contributions to the field, including temporal difference learning and policy gradient...

Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves...

model being used. Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned...

agents or humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek information to improve its preferences....

of fully self-contained autoencoder training. In reinforcement learning, self-supervising learning from a combination of losses can create abstract representations...

professor at University College London. He has led research on reinforcement learning with AlphaGo, AlphaZero and co-lead on AlphaStar. He studied at...