arXiv research

Study differential privacy in multi-agent RL, achieving efficient and private learning.

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal pol…

New algorithm finds Nash equilibrium in multi-agent games.

Paper tackles RL for power grid topology optimization.

MASA framework uses RL to balance portfolio returns and risks.

AutoDIME automates design of multi-agent environments for RL.

In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only…

Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current methods often fail to efficiently learn policies. We thus investigate the presence of a common weakness in single-agent RL, namely value fu…

We propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to environment changes during execution. This violates the Markov assumption that governs most single-agent RL methods and is one of the key c…

We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The interactions between the cells, most notably due to their coupled interference render this optimization prohibitively complex. Utilizing a single agent rei…

Agents learn social skills from each other, improving performance.

The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.

This paper combines RL with CPPI and TIPP for better trading strategies.

A simple DQN-based multi-agent RL system for binary actions.

Agents learn to give rewards to others in a shared learning environment.

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action sp…

New algorithm tackles non-stationary RL with near-optimal regret bounds.

Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like \textit{Go}. While R…

RL framework optimizes trading costs in noisy markets.

This work tackles robust RL in multi-agent settings, improving sample efficiency.

The paper simplifies multi-agent RL dynamics in finite-state Markov games using homogenization.

The paper uncovers the mathematical structure enabling value decomposition in multi-agent systems.

A new method improves ridesharing efficiency using QMIX.

Study multi-agent RL in OTC markets, learning from agents' interactions.

IG-RL learns adaptive traffic signals for any network, outperforming existing methods.

VPP learns joint policies for multi-agent RL through interactions.

Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcem…

RL agents optimize order execution in a realistic market simulation.

The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep…

This paper optimizes model-based RL for two-player zero-sum games with near-optimal sample complexity.

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep…

Recent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives in practical inference of behavioural economy in financial markets and microstructure study. We here present the latest results from a recently published stock market simulator built around a multi-agent s…

OOM-RL uses financial market losses to align AI agents in autonomous systems.

Proposes ABIDES-Gym for financial markets simulation.

RL applied to finance tasks, highlighting challenges and future directions.

Survey of RL methods for control systems with time delays.

Modelling and exploiting teammates' policies in cooperative multi-agent systems have long been an interest and also a big challenge for the reinforcement learning (RL) community. The interest lies in the fact that if the agent knows the teammates' policies, it can adjust its own policy accordingly to arrive at proper c…

MSPM uses modular agents to manage financial portfolios efficiently.

Unfair stock trading strategies have been shown to be one of the most negative perceptions that customers can have concerning trading and may result in long-term losses for a company. Investment banks usually place trading orders for multiple clients with the same target assets but different order sizes and diverse req…

Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions. Recent studies applying deep reinforcement learning (RL) to optimize single traffic lights have shown significant improvement over conventional control. However, o…

This research tackles information design in multi-agent reinforcement learning.

Study uses AUVs and RL to map river plumes over multiple days.

Framework for multi-agent RL with human feedback in a Snake game.

A deep RL framework optimizes resource allocation in wireless networks.

Study models crypto markets using multi-agent reinforcement learning.

The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT. In this paper, we consider a voting-based MARL problem, in which the agents vote to make grou…

New algorithm tackles multi-agent reinforcement learning with optimal convergence rate.

Deep RL improves Diplomacy performance, outperforming previous methods.