Vector td 32/2/2024 P a ( s, s ′ ) = Pr ( s t + 1 = s ′ ∣ s t = s, a t = a ) is the discount-rate.a set of environment and agent states, S.In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.īasic reinforcement learning is modeled as a Markov decision process (MDP): The problems of interest in reinforcement learning have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, or neuro-dynamic programming. The typical framing of a Reinforcement Learning (RL) scenario: an agent takes actions in an environment, which is interpreted into a reward and a representation of the state, which are fed back into the agent.ĭue to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible. The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning ( RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
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