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Found 16 papers, 0 papers with code
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
We consider the task of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses.
The Power of Regularization in Solving Extensive-Form Games
Second, we show that regularized counterfactual regret minimization (\texttt{Reg-CFR}), with a variant of optimistic mirror descent algorithm as regret-minimizer, can achieve $O(1/T^{1/4})$ best-iterate, and $O(1/T^{3/4})$ average-iterate convergence rate for finding NE in EFGs.
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent
A conceptually appealing approach for learning Extensive-Form Games (EFGs) is to convert them to Normal-Form Games (NFGs).
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
This improves upon the best known sample complexity of $\widetilde{\mathcal{O}}((X^2A+Y^2B)/\varepsilon^2)$ by a factor of $\widetilde{\mathcal{O}}(\max\{X, Y\})$, and matches the information-theoretic lower bound up to logarithmic factors.
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL
We design a new class of fully decentralized algorithms -- V-learning, which provably learns Nash equilibria (in the two-player zero-sum setting), correlated equilibria and coarse correlated equilibria (in the multiplayer general-sum setting) in a number of samples that only scales with $\max_{i\in[m]} A_i$, where $A_i$ is the number of actions for the $i^{\rm th}$ player.
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Modern reinforcement learning (RL) commonly engages practical problems with large state spaces, where function approximation must be deployed to approximate either the value function or the policy.
Provably Efficient Algorithms for Multi-Objective Competitive RL
To our knowledge, this work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal.
Online Learning in Unknown Markov Games
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable.
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
However, for multi-agent reinforcement learning in Markov games, the current best known sample complexity for model-based algorithms is rather suboptimal and compares unfavorably against recent model-free approaches.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
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Near-Optimal Reinforcement Learning with Self-Play
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games.
A General Framework for Analyzing Stochastic Dynamics in Learning Algorithms
In this work, we present a streamlined three-step recipe to tackle the "chicken and egg" problem and give a general framework for analyzing stochastic dynamics in learning algorithms.
Reward-Free Exploration for Reinforcement Learning
We give an efficient algorithm that conducts $\tilde{\mathcal{O}}(S^2A\mathrm{poly}(H)/\epsilon^2)$ episodes of exploration and returns $\epsilon$-suboptimal policies for an arbitrary number of reward functions.
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses.
Efficient Policy Learning for Non-Stationary MDPs under Adversarial Manipulation
A Markov Decision Process (MDP) is a popular model for reinforcement learning.
Near Optimal Stratified Sampling
The performance of a machine learning system is usually evaluated by using i. i. d.\ observations with true labels.
Entropy Rate Estimation for Markov Chains with Large State Space
For estimating the Shannon entropy of a distribution on $S$ elements with independent samples, [Paninski2004] showed that the sample complexity is sublinear in $S$, and [Valiant--Valiant2011] showed that consistent estimation of Shannon entropy is possible if and only if the sample size $n$ far exceeds $\frac{S}{\log S}$.
