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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 .
There is also negative reinforcement, which involves taking away an undesirable stimulus. An example of negative reinforcement would be taking an aspirin to relieve a headache. Reinforcement is an important component of operant conditioning and behavior modification. The concept has been applied in a variety of practical areas, including ...
Reinforcement learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. Reinforcement learning is a behavioral learning ...
Many applications of reinforcement learning do not involve just a single agent, but rather a collection of agents that learn together and co-adapt. These agents may be competitive, as in many games, or cooperative as in many real-world multi-agent systems. Multi-agent reinforcement learning studies the problems introduced in this setting.
Unlike behaviorism, in which learning is directly influenced by reinforcement and punishment, social learning theory suggests that watching others be rewarded and punished can indirectly influence behavior. [14] This is known as vicarious reinforcement.
For example, at the start of a game, this would be the matchbox for an empty grid. The tray would be removed and lightly shaken so as to move the beads around. [ 4 ] Then, the bead that had rolled into the point of the "V" shape at the front of the tray was the move MENACE had chosen to make. [ 4 ]
For more than 33 hours a week in the specialized therapy, his clinicians broke down the learning process into basic steps, using repetition and positive reinforcement to affirm behaviors.