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Found 16 papers, 3 papers with code
$\mathbf{(N,K)}$-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model
Recent advances in reinforcement learning (RL) algorithms aim to enhance the performance of language models at scale.
Learning Optimal Tax Design in Nonatomic Congestion Games
We study how to learn the optimal tax design to maximize the efficiency in nonatomic congestion games.
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
Markov Games (MG) is an important model for Multi-Agent Reinforcement Learning (MARL).
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Off-policy dynamic programming (DP) techniques such as $Q$-learning have proven to be important in sequential decision-making problems.
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested is small, and the existence of multiple optimal solutions (equilibria) in stationary games poses extra challenges.
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
In contrast, existing works for Markov games with function approximation have sample complexity bounds scale with the size of the \emph{joint action space} when specialized to the canonical tabular Markov game setting, which is exponentially large in the number of agents.
Offline congestion games: How feedback type affects data coverage requirement
Starting from the facility-level (a. k. a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and give a pessimism-type algorithm that can recover an approximate NE.
Learning in Congestion Games with Bandit Feedback
We propose a centralized algorithm for Markov congestion games, whose sample complexity again has only polynomial dependence on all relevant problem parameters, but not the size of the action set.
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
Furthermore, for offline multi-agent general-sum Markov games, based on the strategy-wise bonus and a novel surrogate function, we give the first algorithm whose sample complexity only scales $\sum_{i=1}^mA_i$ where $A_i$ is the action size of the $i$-th player and $m$ is the number of players.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
On Gap-dependent Bounds for Offline Reinforcement Learning
This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning.
When is Offline Two-Player Zero-Sum Markov Game Solvable?
We study what dataset assumption permits solving offline two-player zero-sum Markov games.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle.
NG+ : A Multi-Step Matrix-Product Natural Gradient Method for Deep Learning
In this paper, a novel second-order method called NG+ is proposed.
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
To achieve the desired result, we develop 1) a new clipping operation to ensure both the probability of being optimistic and the probability of being pessimistic are lower bounded by a constant, and 2) a new recursive formula for the absolute value of estimation errors to analyze the regret.
Minimax Sample Complexity for Turn-based Stochastic Game
The empirical success of Multi-agent reinforcement learning is encouraging, while few theoretical guarantees have been revealed.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
However, the understanding of the sample optimality of model-based RL is still largely missing, even for the linear case.
