Reinforcement Learning for Mean Field Games with Strategic Complementarities

• URL: http://arxiv.org/abs/2006.11683v3
• Date: Mon, 1 Feb 2021 17:14:51 GMT
• Title: Reinforcement Learning for Mean Field Games with Strategic Complementarities
• Authors: Kiyeob Lee, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai
• Abstract summary: We introduce a natural refinement to the equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE) We propose a simple algorithm for computing T-MFE under a known model. We also introduce a model-free and a model-based approach to learning T-MFE and provide sample complexities of both algorithms.
• Score: 10.281006908092932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mean Field Games (MFG) are the class of games with a very large number of agents and the standard equilibrium concept is a Mean Field Equilibrium (MFE). Algorithms for learning MFE in dynamic MFGs are unknown in general. Our focus is on an important subclass that possess a monotonicity property called Strategic Complementarities (MFG-SC). We introduce a natural refinement to the equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE), which allows agents to employ a measure of randomization while accounting for the impact of such randomization on their payoffs. We propose a simple algorithm for computing T-MFE under a known model. We also introduce a model-free and a model-based approach to learning T-MFE and provide sample complexities of both algorithms. We also develop a fully online learning scheme that obviates the need for a simulator. Finally, we empirically evaluate the performance of the proposed algorithms via examples motivated by real-world applications.

Related papers

• Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning [50.92957910121088]
  This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS) For episodic two-player zero-sum MGs, we present three sample-efficient algorithms for learning Nash equilibrium. We extend Reg-MAIDS to multi-player general-sum MGs and prove that it can learn either the Nash equilibrium or coarse correlated equilibrium in a sample efficient manner.
  arXiv  Detail & Related papers  (2024-04-30T06:48:56Z)
• Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL [57.745700271150454]
  We study the sample complexity of reinforcement learning in Mean-Field Games (MFGs) with model-based function approximation. We introduce the Partial Model-Based Eluder Dimension (P-MBED), a more effective notion to characterize the model class complexity.
  arXiv  Detail & Related papers  (2024-02-08T14:54:47Z)
• MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning [20.167933675945324]
  We propose a novel Mixed-strategy Adrial Training algorithm (MAT) for adversarial training. MAT significantly outperforms the state-of-the-art methods on both the GLUE and ANLI benchmarks in terms of generalization and robustness.
  arXiv  Detail & Related papers  (2023-06-27T23:19:53Z)
• On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation [20.66437196305357]
  We study the fundamental statistical efficiency of Reinforcement Learning in Mean-Field Control (MFC) and Mean-Field Game (MFG) with general model-based function approximation. We introduce a new concept called Mean-Field Model-Based Eluder Dimension (MF-MBED), which characterizes the inherent complexity of mean-field model classes.
  arXiv  Detail & Related papers  (2023-05-18T20:00:04Z)
• Scalable Deep Reinforcement Learning Algorithms for Mean Field Games [60.550128966505625]
  Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs has gained momentum, particularly using model-free reinforcement learning (RL) methods. Existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values. We propose two methods to address this shortcoming. The first one learns a mixed strategy from distillation of historical data into a neural network and is applied to the Fictitious Play algorithm. The second one is an online mixing method based on
  arXiv  Detail & Related papers  (2022-03-22T18:10:32Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• Reinforcement Learning for Mean Field Games, with Applications to Economics [0.0]
  Mean field games (MFG) and mean field control problems (MFC) are frameworks to study Nash equilibria or social optima in games with a continuum of agents. We present a two timescale approach with RL for MFG and MFC, which relies on a unified Q-learning algorithm.
  arXiv  Detail & Related papers  (2021-06-25T16:45:04Z)
• Adversarial Inverse Reinforcement Learning for Mean Field Games [17.392418397388823]
  Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems. This paper proposes a novel framework, Mean-Field Adversarial IRL (MF-AIRL), which is capable of tackling uncertainties in demonstrations.
  arXiv  Detail & Related papers  (2021-04-29T21:03:49Z)
• Model-free Representation Learning and Exploration in Low-rank MDPs [64.72023662543363]
  We present the first model-free representation learning algorithms for low rank MDPs. Key algorithmic contribution is a new minimax representation learning objective. Result can accommodate general function approximation to scale to complex environments.
  arXiv  Detail & Related papers  (2021-02-14T00:06:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.