A decentralized deep RL controller improves hexapod locomotion learning.
problem Deep RL struggles with real-world legged robot control.
method Decentralized deep RL on a hexapod robot.
result Decentralized approach learns better and faster.
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48 results for “Decentralized Reinforcement Learning”
This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.
problem Allocating heterogeneous resources among multiple agents in a decentralized manner.
method Liquid-Graph-Time Clustering-IPPO, integrating dynamic cluster consensus.
result LGTC-IPPO achieves more stable rewards, better coordination, and robust performance.
Flexible decentralized MARL framework for cooperative multi-agent learning.
problem Complexity and impracticality of centralized MARL in complicated applications.
method Flexible fully-decentralized actor-critic MARL framework using primal-dual hybrid gradient descent.
result Competitive performance in large-scale cooperative multi-agent environments.
A decentralized approach for agents to learn and optimize collectively.
problem Challenges in coordinating non-cooperative agents to solve complex sequential decision problems.
method Designing a learning environment where agents learn by trading and optimizing local objectives, leading to a Nash equilibrium.
result Decentralized reinforcement learning algorithms that can handle various decision-making scenarios.
DePAint solves MARL for agents with local constraints, privacy, and no central controller.
problem Training multi-agent systems to optimize rewards while adhering to safety constraints in a decentralized setting.
method Formulated as a decentralized constrained multi-agent Markov Decision Problem, proposed DePAint method using momentum-based decentralized policy gradient.
result First privacy-preserving fully decentralized MARL algorithm considering both peak and average constraints.
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlcond-mat.stat-mech
Study uses multi-agent reinforcement learning to control self-assembly with high-resolution external control.
problem Designing effective external control protocols for self-assembly with high-resolution control.
method Investigated a multi-agent reinforcement learning approach, comparing fully decentralized and partially decentralized strategies.
result Partially decentralized approach outperforms fully decentralized in controlling self-assembly towards target structures.
A decentralized routing framework for lunar exploration robots.
problem Routing data in intermittent connectivity lunar networks.
method Graph Attention-based Multi-Agent Reinforcement Learning (GAT-MARL).
result Higher delivery rates, no duplications, fewer packet losses.
A new method for learning policies in multiple environments.
problem Learning policies that work in different but related environments.
method Decentralized entropy-regularized policy gradient method.
result The method can learn effective policies in multiple environments.
Improved exploration in cooperative multi-agent reinforcement learning.
problem Limited expressiveness of Gaussian policies in DecSPG hinders effective exploration.
method Proposes decentralized diffusion policy learning (DDPL) with denoising diffusion probabilistic models.
result Consistently improved performance on various MARL benchmarks.
Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents. Motivated by decentralized applications such as sensor networks, swarm robotics, and power grids, we study policy evaluation in MARL, where agents with jo…
Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challenging problem. In this work, we address this problem by providing a finite-sample analysis for decentralized batch MARL with networked agents…
New MARL algorithms resolve the curse of multiagency with function approximation.
problem Challenges in Multi-Agent Reinforcement Learning (MARL) due to the curse of multiagency.
method V-Learning with Policy Replay and Decentralized Optimistic Policy Mirror Descent.
result First polynomial sample complexity results for learning approximate Coarse Correlated Equilibria (CCEs) of Markov Games under decentralized linear function approximation.
We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment. The objective is to classify an image over a finite time horizon. We propose a network architecture on how agents should form a local belief, take local actions, an…
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…
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learningcs.MA
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communicatio…
Proposes three decentralized multi-agent reinforcement learning algorithms to reduce network congestion.
problem Finding a joint policy maximizing long-term return in a decentralized multi-agent system.
method Three fully decentralized multi-agent natural actor-critic (MAN) algorithms using linear function approximations.
result The proposed algorithms achieve performance comparable to or better than standard methods in reducing network congestion.
We consider the problem of \emph{fully decentralized} multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network. Specifically, we assume that the reward functions of the agents might correspond to different tasks, and are only known to the corresponding…
A new method improves ridesharing efficiency using QMIX.
problem Improving ridesharing dispatch efficiency with complex environments.
method QMIX for centralized training with decentralized execution.
result QMIX outperforms IDQN in various scenarios.
V-learning tackles multiagent reinforcement learning by reducing sample complexity.
problem Curse of multiagents in multiagent reinforcement learning.
method V-learning is a fully decentralized algorithm that learns Nash, correlated, and coarse correlated equilibria.
result V-learning achieves sample complexity that scales with the maximum number of actions per agent, not the joint action space.
Improves data efficiency in multi-agent control tasks using model-based reinforcement learning.
problem Limited data efficiency in reinforcement learning for multi-agent tasks.
method Decentralized model-based policy optimization (DMPO) framework.
result DMPO achieves superior data efficiency and matches model-free methods using true models.
This paper proposes a gossip-based algorithm for distributed bilevel optimization over networks.
problem Distributed bilevel optimization over networks with limited communication.
method Gossip-based distributed bilevel learning algorithm.
result Achieves optimal sample complexities for general and strongly convex objectives.
Enhances crypto-asset AMM with deep learning for better liquidity and efficiency.
problem Reduced slippage and improved liquidity in decentralized finance.
method Deep reinforcement learning for predicting market equilibrium and optimizing liquidity.
result Improved capital efficiency and reduced slippage for crypto-asset traders.
Paper tackles low sample and communication complexities in decentralized bilevel optimization.
problem Decentralized bilevel optimization problems with limited computation and communication capabilities.
method Proposes INTERACT and SVR-INTERACT algorithms to achieve low sample and communication complexities.
result Achieves both low sample and communication complexities for solving decentralized bilevel optimization problems.
We consider the networked multi-agent reinforcement learning (MARL) problem in a fully decentralized setting, where agents learn to coordinate to achieve the joint success. This problem is widely encountered in many areas including traffic control, distributed control, and smart grids. We assume that the reward functio…
Smart grid uses deep learning to optimize household energy use.
problem Optimizing household energy use under real-time pricing schemes.
method Multi-agent deep actor-critic learning for decentralized agents with partial observability.
result Deep reinforcement learning reduces peak-to-average energy consumption and costs.
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their ac…
New algorithm reduces learning regret in multi-agent systems with unknown dynamics.
problem Challenges in decentralized learning due to unknown dynamics and lack of communication.
method Proposed MARL algorithm for two-agent LQ systems with unknown dynamics and one-directional communication.
result Achieved regret bound for multi-agent LQ systems with certain communication patterns.
Bayesian network approach for efficient cooperative MARL.
problem Leveraging inter-agent coupling information for scalable MARL algorithms.
method Modeling cooperative MARL via Bayesian networks, identifying value dependency sets, proposing P-DTDE paradigm.
result P-DTDE policy gradient estimator has lower total variance than CTDE.
Paper analyzes convergence of decentralized algorithms with noise and bias.
problem Finite time convergence analysis of decentralized stochastic approximation schemes.
method Separated iterates into consensual parts and consensus error; bounded consensus error in terms of stationarity.
result Decentralized SA scheme converges at rate.
Policy-gradient method controls multiple non-cohesive targets.
problem Controlling multiple non-cohesive targets in a decentralized manner.
method Proximal Policy Optimization for target selection and driving.
result Effective control of non-cohesive targets without prior dynamics knowledge.
Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. Nevertheless, by considering a MEC system consisting of mu…
Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control. With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis…
We propose a method to model multi-agent behaviors with limited observation and mechanical constraints.
problem Modeling real-world multi-agent behaviors with limited observation and mechanical constraints.
method Decentralized generative models with partial observation and mechanical constraints based on hierarchical variational recurrent neural networks.
result Our method effectively models and predicts biologically plausible behaviors with minimal constraint violations.
Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximati…
Autogov uses RL to automate DeFi governance, improving security and profitability.
problem Manual DeFi governance is prone to human bias and financial risks.
method Autogov employs a deep Q-network reinforcement learning strategy for semi-automated parameter adjustments.
result Autogov outperforms traditional governance methods by at least 14% in terms of protocol profitability.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcs.LG
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones f…
MARLA uses deep reinforcement learning for multi-agent AHT, reducing Bayes risk.
problem Decentralized active hypothesis testing among multiple agents.
method MARLA algorithm using deep multi-agent reinforcement learning.
result MARLA outperforms single-agent learning in AHT problems.
In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learning process, agents may link these interruptions, that impact the…
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
Deep reinforcement learning boosts throughput in RF-powered cognitive radio networks.
problem Maximizing throughput in large-scale, decentralized RF-powered cognitive radio networks.
method Proposes deep reinforcement learning to find optimal policies for network throughput maximization.
result Deep reinforcement learning outperforms existing techniques in large-scale RF-CRN environments.
Deep reinforcement learning (DRL) is a booming area of artificial intelligence. Many practical applications of DRL naturally involve more than one collaborative learners, making it important to study DRL in a multi-agent context. Previous research showed that effective learning in complex multi-agent systems demands fo…
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions t…
Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential consequences of others' behaviors so as to plan ahead. It is known that humans use such reasoning ability recursively by considering what others …
MaxMax Q-Learning improves coordination in multi-agent reinforcement learning by refining action selection.
problem Relative over-generalization in decentralized multi-agent reinforcement learning.
method MaxMax Q-Learning employs iterative sampling and evaluation of potential next states to refine approximations of ideal state transitions.
result MaxMax Q-Learning frequently outperforms existing baselines, demonstrating enhanced convergence and sample efficiency.
When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the other agents. Recently proposed deep multi-agent reinforcement learning methods have tried to mitigate this non-stationarity by attempting t…
New algorithm reduces complexity in multi-agent reinforcement learning.
problem High computational complexity in exact computations for multi-agent reinforcement learning.
method Design of a scalable algorithm based on Natural Policy Gradient, using local information and limited communication.
result Converges to globally optimal policy with dimension-free complexity and localization error.
QLAMMP optimizes fees on AMMs using Q-Learning.
problem Static AMMs cannot adapt to market changes, leading to high slippage.
method Developed a Q-Learning Agent (QLAMMP) to learn optimal fee rates.
result QLAMMP consistently outperforms static AMMs under various market conditions.
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large…