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...

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

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

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

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

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...

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...

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

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

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

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

report on the Kimi K1.5 model, Moonshot researchers outline their reinforcement learning methods, which they claim enabled the model to achieve state-of-the-art...

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

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

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...

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

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...

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

The company has created many neural network models trained with reinforcement learning to play video games and board games. It made headlines in 2016 after...

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

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...