Algorithms

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Browse subtopics: Reinforcement Learning

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Actor-critic methods

Actor-critic methods are a family of reinforcement learning algorithms that learn two things at once: a parameterized policy, called the actor, which chooses actions, and a value function, called the critic

Deep Learning Machine Learning

AlphaDev

AlphaDev is an artificial intelligence system built by Google DeepMind that used deep reinforcement learning to discover faster algorithms for common computing tasks, most notably small-scale sorting and…

Google DeepMind Reinforcement Learning

Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm for sequential decision-making that finds strong actions by running many simulated playthroughs of a problem and aggregating their outcomes into a…

AI in Gaming Reinforcement Learning

Multi-armed bandit

A multi-armed bandit is a sequential decision problem in which a learner repeatedly picks one action from a fixed set, receives a random reward for that action only, and tries to accumulate as much reward as…

Machine Learning Reinforcement Learning

Nash equilibrium

A Nash equilibrium is a combination of strategies, one for each player in a game, such that no player can raise their own payoff by changing strategy alone while everyone else keeps theirs unchanged.

Computer Science Mathematics

Soft Actor-Critic

Soft Actor-Critic (SAC) is an off-policy, maximum-entropy deep reinforcement learning algorithm that trains a stochastic actor-critic to maximize expected reward plus the entropy of its own policy, so the…

Deep Learning Reinforcement Learning

Temporal-difference learning

Temporal-difference (TD) learning is a class of model-free reinforcement learning methods that learn value-function estimates by bootstrapping: updating each estimate of how good a state is toward a target…

Machine Learning Reinforcement Learning

Twin Delayed DDPG

Twin Delayed Deep Deterministic Policy Gradient (TD3) is an off-policy actor-critic reinforcement learning algorithm for continuous action spaces, introduced by Scott Fujimoto, Herke van Hoof, and David Meger…

Deep Learning Reinforcement Learning