Multi-agent Reinforcement Learning
389 papers with code • 3 benchmarks • 9 datasets
The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.
Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Benchmarks
These leaderboards are used to track progress in Multi-agent Reinforcement Learning
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Libraries
Use these libraries to find Multi-agent Reinforcement Learning models and implementationsDatasets
CityFlow
StarCraft II Learning Environment
SMAC-Exp
Hanabi Learning Environment
OG-MARL
pursuitMW
ColosseumRL
Latest papers
Modelling crypto markets by multi-agent reinforcement learning
Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022.
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games
Under a set of assumptions, we prove the convergence of the algorithm to a subjective notion of Markov perfect Nash equilibrium in NAMGs.
Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing
jw3il/graph-marl
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The size of the observed neighborhood limits the generalizability to different graphs and affects the reactivity of agents, the quality of the selected actions, and the communication overhead.
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management
steamerlee/masa
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Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years.
Fully Independent Communication in Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems.
Emergent Dominance Hierarchies in Reinforcement Learning Agents
Modern Reinforcement Learning (RL) algorithms are able to outperform humans in a wide variety of tasks.
Measuring Policy Distance for Multi-Agent Reinforcement Learning
harry67hu/madps
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Furthermore, we extend MAPD to a customizable version, which can quantify differences among agent policies on specified aspects.
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
buaawgj/tace
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Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces.
Context-aware Communication for Multi-agent Reinforcement Learning
lxxxxr/cacom
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Following this, agents utilize attention mechanisms in the second stage to selectively generate messages personalized for the receivers.
