Reinforcement Learning System
A Reinforcement Learning System is a online reward-maximization system that implements a reinforcement learning algorithm to solve a reinforcement learning task (to learn a policy to maximize reward from feedback data).
- Context:
- It can (typically) face challenges like exploration-exploitation trade-offs, sparse reward spaces, or non-stationary environments.
- It can (often) incorporate a Markov Decision Process (MDP), where the environment is modeled as a series of states and transitions governed by probabilities.
- It can (often) leverage temporal difference learning methods to balance short-term and long-term rewards.
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- It can be based on a Sequential Decision-Making System, where the system learns from a series of decisions made in an evolving environment.
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- Example(s):
- an AI-Driven Reinforcement Learning-Based System that learns through interactive feedback to optimize a complex decision process.
- an Apprenticeship Learning System that learns a policy by observing and imitating an expert's behavior.
- an Inverse Reinforcement Learning System, which infers a reward function based on observed optimal behavior.
- an Instance-Based Reinforcement Learning System, which leverages past experiences to guide future decisions.
- an Average-Reward Reinforcement Learning System that aims to optimize long-term average rewards instead of cumulative rewards.
- a Distributed Reinforcement Learning System that scales learning across multiple agents or processors.
- a Temporal Difference Learning System that updates value estimates based on the difference between predicted and observed rewards.
- a Q-Learning System that learns a policy by updating Q-values based on observed transitions.
- a SARSA System, similar to Q-Learning but updates based on the action actually taken in the next state.
- a Relational Reinforcement Learning System, which incorporates relational information to learn structured policies.
- a Gaussian Process Reinforcement Learning System that uses Gaussian processes for value estimation.
- a Hierarchical Reinforcement Learning System, which decomposes the main task into a hierarchy of sub-tasks with separate sub-policies.
- an Associative Reinforcement Learning System that associates actions with rewards using learned associations.
- a Bayesian Reinforcement Learning System, which incorporates uncertainty in model parameters using Bayesian approaches.
- a Radial Basis Function Network that approximates value functions using radial basis functions.
- a Policy Gradient Reinforcement Learning System that directly optimizes the policy using gradient-based methods.
- a Least Squares Reinforcement Learning System, which minimizes prediction error using least squares methods.
- an Evolutionary Reinforcement Learning System that applies evolutionary algorithms to discover optimal policies.
- a Reward Shaping System that modifies the reward structure to make learning more efficient.
- a PAC-MDP Learning System that ensures near-optimal performance within a specified confidence bound.
- a Reinforcement Learning-based Recommendation System that dynamically optimizes content recommendations based on user interaction.
- a Deep Reinforcement Learning System, such as AlphaGo, that uses deep neural networks to handle high-dimensional inputs.
- a CogitAI Continua SaaS Platform [1], which provides a framework for continuous learning.
- an AlphaProof System used for automated theorem proving through reinforcement learning.
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- Counter-Example(s):
- an Unsupervised Learning System that learns patterns from data without explicit feedback.
- a Supervised Learning System, such as a semi-supervised learning system, that learns from labeled data instead of interacting with an environment.
- See: Active Learning System, Online Learning System, Machine Learning System, Value Function Approximation System, Markov Decision Process.
References
2017
- (Stone, 2017) ⇒ Stone P. (2017) Reinforcement Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA pp. 1088-1090
- QUOTE: Reinforcement Learning describes a large class of learning problems characteristic of autonomous agents interacting in an environment: sequential decision-making problems with delayed reward. Reinforcement-learning algorithms seek to learn a policy (mapping from states to actions) that maximizes the reward received over time.
Unlike in supervised learning problems, in reinforcement-learning problems, there are no labeled examples of correct and incorrect behavior. However, unlike unsupervised learning problems, a reward signal can be perceived.
- QUOTE: Reinforcement Learning describes a large class of learning problems characteristic of autonomous agents interacting in an environment: sequential decision-making problems with delayed reward. Reinforcement-learning algorithms seek to learn a policy (mapping from states to actions) that maximizes the reward received over time.
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Reinforcement_learning Retrieved:2017-12-24.
- Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.
In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[1] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs.
- Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.