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Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .
Shaping sometimes fails. An oft-cited example is an attempt by Marian and Keller Breland (students of B.F. Skinner) to shape a pig and a raccoon to deposit a coin in a piggy bank, using food as the reinforcer. Instead of learning to deposit the coin, the pig began to root it into the ground, and the raccoon "washed" and rubbed the coins together.
When the correct action is performed the animal receives positive reinforcement in the form of food or other reward. In some cases, the chamber may deliver positive punishment to discourage incorrect responses. For example, researchers have tested certain invertebrates' reaction to operant conditioning using a "heat box". [11]
For example, having been trained to peck at "red" a pigeon might also peck at "pink", though usually less strongly. Context refers to stimuli that are continuously present in a situation, like the walls, tables, chairs, etc. in a room, or the interior of an operant conditioning chamber. Context stimuli may come to control behavior as do ...
For example, the outcome of a game (i.e., whether one player won or lost) can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation ( gradient descent ...
Social learning theory is a theory of social behavior that proposes that new behaviors can be acquired by observing and imitating others. It states that learning is a cognitive process that takes place in a social context and can occur purely through observation or direct instruction, even in the absence of motor reproduction or direct reinforcement. [1]
The mountain car problem, although fairly simple, is commonly applied because it requires a reinforcement learning agent to learn on two continuous variables: position and velocity. For any given state (position and velocity) of the car, the agent is given the possibility of driving left, driving right, or not using the engine at all.