Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions...

Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves...

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...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

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

a normal (non-LLM) reinforcement learning agent. Alternatively, it can propose increasingly difficult tasks for curriculum learning. Instead of outputting...

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...

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

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

revolutionized the study of reinforcement learning and decision making over the four decades. In 1988, Sutton described machine learning in terms of decision...

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

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...

foundational contributions to the field of modern computational reinforcement learning. Andrew Gehret Barto was born in either 1948 or 1949. He received...

even without physical practice or direct reinforcement. In addition to the observation of behavior, learning also occurs through the observation of rewards...

next token. After this step, the model was then fine-tuned with reinforcement learning feedback from humans and AI for human alignment and policy compliance...

International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is...

hyperparameter optimization and meta-learning and is a subfield of automated machine learning (AutoML). Reinforcement learning (RL) can underpin a NAS search...

contrast to traditional learning techniques which rely on supervised learning approaches that are less flexible, reinforcement learning recommendation techniques...

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

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

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

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

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

deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents...

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

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

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

generation. Maluuba published a research paper learning dialogue policies with deep reinforcement learning. In 2016, Maluuba also freely released the Frames...