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# Meta-Reinforcement Learning

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arXiv Nov 6, 2026
Opponent shaping without peeking at opponents: a meta-learned update rule tops a five-game tournament
Methods that shape how an opponent learns in general-sum games have needed access to that opponent's policy, parameters or learning rule. A study carries the single-agent practice of meta-learning an update rule into multi-agent play, meta-training a rule that reads only the agent's own experience and payoff matrix, so deployment needs no privileged opponent information. Across round-robin tournaments in Prisoner's Dilemma, Matching Pennies, Chicken, Stag Hunt and Coin Game, the rule ranks first in normalised mean score over all five games and first in self-play.
Methods that shape how an opponent learns in general-sum games have needed access to that opponent's policy, parameters or learning rule. A study carries the single-agent practice of meta-learning an update rule into multi-agent play, meta-training a rule that reads only the agent's own experience and payoff matrix, so deployment needs no privileged opponent information. Across round-robin tournaments in Prisoner's Dilemma, Matching Pennies, Chicken, Stag Hunt and Coin Game, the rule ranks first in normalised mean score over all five games and first in self-play.
Hong kyu Lee（First author）
Discovered Update Rule for Opponent Shaping Opponent Shaping Meta-Reinforcement Learning General-Sum Games
发表出处待核验 Sep 27, 2026
A PhD thesis proposes three methodological innovations and a design framework for bringing causal machine learning into clinical practice, testing continuous treatment models on a physiotherapy dosage case
This PhD thesis addresses how causal machine learning can be translated into real-world clinical practice along three lines: for binary treatment effect estimation in clinical contexts it identifies eight key decision points that directly influence treatment effect estimates and proposes a structured framework for designing causal models in clinical settings; for the difficulty of verifying counterfactual predictions it proposes a novel method that grounds average model predictions in a group-level quantity that can be empirically verified using randomized or real-world clinical trial data, and shows that a model with a lower counterfactual error bound can be constructed and that information bottleneck regularization improves treatment effect estimation; for continuous causal machine learn
This PhD thesis addresses how causal machine learning can be translated into real-world clinical practice along three lines: for binary treatment effect estimation in clinical contexts it identifies eight key decision points that directly influence treatment effect estimates and proposes a structured framework for designing causal models in clinical settings; for the difficulty of verifying counterfactual predictions it proposes a novel method that grounds average model predictions in a group-level quantity that can be empirically verified using randomized or real-world clinical trial data, and shows that a model with a lower counterfactual error bound can be constructed and that information bottleneck regularization improves treatment effect estimation; for continuous causal machine learn
Louk van Remmerden（First author, Corresponding author）
Causal Inference Treatment Effect Estimation Continuous Causal Machine Learning Meta-Reinforcement Learning Information Bottleneck

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