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  2. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    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 ...

  3. Reinforcement - Wikipedia

    en.wikipedia.org/wiki/Reinforcement

    Operant conditioning chamber for reinforcement training. In behavioral psychology, reinforcement refers to consequences that increase the likelihood of an organism's future behavior, typically in the presence of a particular antecedent stimulus. [1] For example, a rat can be trained to push a lever to receive food whenever a light is turned on.

  4. Multi-agent reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Multi-agent_reinforcement...

    Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. [ 1 ] Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the ...

  5. Deep reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Deep_reinforcement_learning

    Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.

  6. Applied behavior analysis - Wikipedia

    en.wikipedia.org/wiki/Applied_behavior_analysis

    Reinforcement occurs when the consequence of a behavior makes it more likely for that behavior to occur in the future. Reinforcing consequences can be either positive, where something preferred is added, or negative, where something aversive is removed. [63] Reinforcement is the key element in operant conditioning and most behavior change programs.

  7. Self-play - Wikipedia

    en.wikipedia.org/wiki/Self-play

    In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning agent on a given task, in cooperation or competition with one or more agents. These agents learn by trial-and-error, and researchers may choose to have the learning algorithm play the role of two or more of the different agents.

  8. Mountain car problem - Wikipedia

    en.wikipedia.org/wiki/Mountain_car_problem

    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.

  9. Reinforcement learning from human feedback - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning...

    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 .