Self-play (reinforcement learning technique)

Short description: Reinforcement learning technique

Machine learning and
data mining

Problems

* Classification
* Clustering
* Regression
* Anomaly detection
* AutoML
* Association rules
* Reinforcement learning
* Structured prediction
* Feature engineering
* Feature learning
* Online learning
* Semi-supervised learning
* Unsupervised learning
* Learning to rank
* Grammar induction

Supervised learning
(classification • regression)

* Decision trees
* Ensembles
* Bagging
* Boosting
* Random forest
* k-NN
* Linear regression
* Naive Bayes
* Artificial neural networks
* Logistic regression
* Perceptron
* Relevance vector machine (RVM)
* Support vector machine (SVM)

Clustering

* BIRCH
* CURE
* Hierarchical
* k-means
* Expectation–maximization (EM)

DBSCAN
* OPTICS
* Mean-shift

Dimensionality reduction

* Factor analysis
* CCA
* ICA
* LDA
* NMF
* PCA
* t-SNE

Structured prediction

* Graphical models
* Bayes net
* Conditional random field
* Hidden Markov

Anomaly detection

* k-NN
* Local outlier factor

Artificial neural networks

* Autoencoder
* Deep learning
* DeepDream
* Multilayer perceptron
* RNN
* LSTM
* GRU
* Restricted Boltzmann machine
* GAN
* SOM
* Convolutional neural network
* U-Net

Reinforcement learning

* Q-learning
* SARSA
* Temporal difference (TD)

Theory

* Bias–variance dilemma
* Computational learning theory
* Empirical risk minimization
* Occam learning
* PAC learning
* Statistical learning
* VC theory

Machine-learning venues

* NeurIPS
* ICML
* ML
* JMLR
* ArXiv:cs.LG

Glossary of artificial intelligence

* Glossary of artificial intelligence

Related articles

* List of datasets for machine-learning research
* Outline of machine learning

* v
* t
* e

Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves".

## Contents

* 1 Definition and motivation
* 2 Usage
* 3 Connections to other disciplines
* 4 Further reading
* 5 References

## Definition and motivation

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. When successfully executed, this technique has a double advantage:

1. It provides a straightforward way to determine the actions of the other agents, resulting in a meaningful challenge.
2. It increases the amount of experience that can be used to improve the policy, by a factor of two or more, since the viewpoints of each of the different agents can be used for learning.

## Usage

Self-play is used by the AlphaZero program to improve its performance in the games of chess, shogi and go.[1]

Self-play is also used to train the Cicero AI system to outperform humans at the game of Diplomacy. The technique is also used in training the DeepNash system to play the game Stratego.[2][3]

## Connections to other disciplines

Self-play has been compared to the epistemological concept of tabula rasa that describes the way that humans acquire knowledge from a "blank slate".[4]

## Further reading

* "Survey of Self-Play in Reinforcement Learning". 2021. arXiv:2107.02850.`{{cite arXiv}}`: CS1 maint: missing class (link)

## References

1. ↑ Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen; Hassabis, Demis (5 December 2017). "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm". arXiv:1712.01815 [cs.AI].
2. ↑ Snyder, Alison (2022-12-01). "Two new AI systems beat humans at complex games" (in en). https://www.axios.com/2022/12/01/ai-beats-humans-complex-games.
3. ↑ Erich_Grunewald (in en). Notes on Meta's Diplomacy-Playing AI. https://www.lesswrong.com/posts/oT8fmwWddGwnZbbym/notes-on-meta-s-diplomacy-playing-ai.
4. ↑ Laterre, Alexandre (2018). "Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization". arXiv:1712.01815.`{{cite arXiv}}`: CS1 maint: missing class (link)

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