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...

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

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...

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

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

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

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

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

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...

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

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

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...

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

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

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

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...

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

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

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

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

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

judges most likely to attain the maximum value of +1. Similarly, a reinforcement learning system can have a "reward function" that allows the programmers...

systems where there's no evident labeling or mapping of components. Reinforcement learning is employed to build models that progressively refine their system...

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

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

Waluigi". AI alignment Hallucination Existential risk from AGI Reinforcement learning from human feedback (RLHF) Suffering risks Bereska, Leonard; Gavves...