What is counterfactual regret minimization?
Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR.
Is counterfactual regret minimization reinforcement learning?
Learning from regrets is what Counter Factual Minimization is all about. The notion of “regret” is introduced in the article “Introduction to Regret in Reinforcement Learning”. However, it considers scenarios or games composed of a single step or action.
What is regret minimization?
A regret minimization framework is a business heuristic that enables you to make a decision, by projecting yourself in the future, at an old age, and visualize whether the regrets of missing an opportunity would hunt you down, vs. having taken the opportunity and failed.
What is regret matching?
Regret matching is a widely-used algorithm for learning how to act. We begin by proving that regrets on actions in one setting (game) can be transferred to warm start the regrets for solving a different setting with same structure but differ- ent payoffs that can be written as a function of parameters.
What are payoff and regret functions?
Regret (also called opportunity loss) is defined as the difference between the actual payoff and the payoff that would have been obtained if a different course of action had been chosen. This is also called difference regret. Furthermore, the ratio regret is the ratio between the actual payoff and the best one.
What is CFR poker?
Counterfactual regret minimization (CFR) is an algorithm that approximates a Nash equilibrium. All modern poker AIs use a variant of CFR. The simplest variant and the one discussed here is Vanilla CFR. Intuitively, CFR works by repeatedly playing against itself while minimizing regret.
What is counterfactual thinking in social psychology?
Counterfactual thinking is thinking about a past that did not happen. This is often the case in “if only…” situations, where we wish something had or had not happened.
What is regret in reinforcement learning?
Mathematically speaking, the regret is expressed as the difference between the payoff (reward or return) of a possible action and the payoff of the action that has been actually taken.
What is regret function?
It incorporates a regret term in the utility function which depends negatively on the realized outcome and positively on the best alternative outcome given the uncertainty resolution. This regret term is usually an increasing, continuous and non-negative function subtracted to the traditional utility index.
What is a counterfactual in science?
The term counterfactual is short for “counter-to-fact conditional,” a statement about what would have been true, had certain facts been different—for example, “Had the specimen been heated, it would have melted.” On the face of it, claims about what would or could have happened appear speculative or even scientifically …
What is a counterfactual in research?
Counterfactual analysis enables evaluators to attribute cause and effect between interventions and outcomes. The ‘counterfactual’ measures what would have happened to beneficiaries in the absence of the intervention, and impact is estimated by comparing counterfactual outcomes to those observed under the intervention.
What is minimax regret criteria?
This decision criteria has an objective of minimizing the maximum regret which can occur as a result of choosing a certain option and not the others. This approach uses this formula; Opportunity Loss (OL) = Maximum Payoff – Payoff under Each condition occurrence.
What is regret in algorithm?
The regret of our online algorithm is the difference between the loss of our algorithm and the loss using π. Different notions of regret quantify differently what is considered to be a “simple” alternative policy.
What is regret analysis?
Regret theory states that people anticipate regret if they make the wrong choice, and they consider this anticipation when making decisions. Fear of regret can play a significant role in dissuading someone from taking action or motivating a person to take action.
Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It iteratively traverses the game tree in order to converge to a Nash equilibrium. In order to deal with extremely large games, CFR typically uses domain-specific heuristics to simplify the target game in a process known as abstraction.
What is counter factual minimization?
Learning from regrets is what Counter Factual Minimization is all about. The notion of “regret” is introduced in the article “ Introduction to Regret in Reinforcement Learning ”. However, it considers scenarios or games composed of a single step or action.
What is Hart and Mas-Colell’s regret matching algorithm?
In 2000, Hart and Mas-Colell introduced the important game-theoretic algorithm of regret matching. Players reach equilibrium play by tracking regrets for past plays, making future plays proportional to positive cumulative regrets (i.e. how much they wished they had made the moves on average).
The notion of “regret” is introduced in the article “ Introduction to Regret in Reinforcement Learning ”. However, it considers scenarios or games composed of a single step or action. Certainly, this is not realistic enough, because most scenarios, in reality, are composed of multiple steps.