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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 ...
Human feedback is commonly collected by prompting humans to rank instances of the agent's behavior. [15] [17] [18] These rankings can then be used to score outputs, for example, using the Elo rating system, which is an algorithm for calculating the relative skill levels of players in a game based only on the outcome of each game. [3]
Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
In neuroscience, the reward system is a collection of brain structures and neural pathways that are responsible for reward-related cognition, including associative learning (primarily classical conditioning and operant reinforcement), incentive salience (i.e., motivation and "wanting", desire, or craving for a reward), and positively-valenced emotions, particularly emotions that involve ...
Operant conditioning originated with Edward Thorndike, whose law of effect theorised that behaviors arise as a result of consequences as satisfying or discomforting. In the 20th century, operant conditioning was studied by behavioral psychologists, who believed that much of mind and behaviour is explained through environmental conditioning.
Administrating two reinforcement schedules at the same time Concurrent schedules – A complex reinforcement procedure in which the participant can choose any one of two or more simple reinforcement schedules that are available simultaneously. Organisms are free to change back and forth between the response alternatives at any time.
Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict the output from future input.
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 ...