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

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

Look up reinforcement in Wiktionary, the free dictionary. Reinforcement is a consequence that will strengthen an organism's future behavior whenever that...

Rebar (short for reinforcement bar or reinforcing bar), known when massed as reinforcing steel or steel reinforcement, is a tension device added to concrete...

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

influential experimental work, outlined in their 1957 book Schedules of Reinforcement. Skinner was a prolific author, publishing 21 books and 180 articles...

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

In materials science, reinforcement is a constituent of a composite material which increases the composite's stiffness and tensile strength. Following...

A sound reinforcement system is the combination of microphones, signal processors, amplifiers, and loudspeakers in enclosures all controlled by a mixing...

Reinforcement theory is a limited effects media model applicable within the realm of communication. The theory generally states that people seek out and...

Reinforcement is a process within speciation where natural selection increases the reproductive isolation between two populations of species by reducing...

lightweight, rigid, deployable structures; rapid manufacturing; and dynamic reinforcement. High strain composites are another type of high-performance composites...

compensated for by the inclusion of reinforcement having higher tensile strength or ductility. The reinforcement is usually, though not necessarily, steel...

In behaviorism, rate of reinforcement is number of reinforcements per time, usually per minute. Symbol of this rate is usually Rf. Its first major exponent...

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

Communal reinforcement is a social phenomenon in which a concept or idea is repeatedly asserted in a community, regardless of whether sufficient empirical...

Community Reinforcement Approach and Family Training (CRAFT) is a behaviorial therapy approach for treating drug addiction, developed by Robert J. Meyers...

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...

Reinforcement is a process of speciation where natural selection increases the reproductive isolation (further divided to pre-zygotic isolation and post-zygotic...

The reinforcement of 3D printed concrete is a mechanism where the ductility and tensile strength of printed concrete are improved using various reinforcing...

conversational applications using a combination of supervised learning and reinforcement learning from human feedback. Successive user prompts and replies are...

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

Scleral reinforcement is a surgical procedure used to reduce or stop further macular damage caused by high myopia, which can be degenerative. Myopia is...

The mathematical principles of reinforcement (MPR) constitute of a set of mathematical equations set forth by Peter Killeen and his colleagues attempting...

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

Reinforcement sensitivity theory (RST) proposes three brain-behavioral systems that underlie individual differences in sensitivity to reward, punishment...

The Stochastic Neural Analog Reinforcement Calculator (SNARC) is a neural-net machine designed by Marvin Lee Minsky. Prompted by a letter from Minsky...

through which the strength of a voluntary behavior is modified, either by reinforcement or by punishment. However, classical conditioning can affect operant...

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

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