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Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
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Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
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Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we provide a systematic evaluation and comparison of three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks, and we provide insights regarding the effectiveness of different learning approaches. We open-source EPyMARL, which extends the PyMARL codebase to include additional algorithms and allow for flexible configuration of algorithm implementation details such as parameter sharing. Finally, we open-source two environments for multi-agent research which focus on coordination under sparse rewards.
Forward citations
Cited by 19 Pith papers
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Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
IBAL framework constructs information-theoretic adversarial attacks on agent observations and actions to train MARL agents that remain robust to interaction disruptions and agent-missing scenarios.
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Evo-Memory is a new benchmark for self-evolving memory in LLM agents across task streams, with baseline ExpRAG and proposed ReMem method that integrates reasoning, actions, and memory updates for continual improvement.
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Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning
Certified local LTL_safe contracts, jointly fixed-point checked and selected by a bandit, recover coordinated safe team policies under decentralised multi-agent RL execution.
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CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control
CINOC policies trained on small agent populations exhibit cardinality invariance enabling zero-shot transfer to larger populations in PDE control via mean-field theory.
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Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.
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Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in cooperative MARL.
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Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
MAVIC corrects Bellman backups at instruction boundaries by adjusting the incoming objective and restoring continuation value, enabling consistent estimation under stochastic instruction switching in a unified policy.
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DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games
Team-symmetric games always have team-symmetric Nash equilibria solvable via linear complementarity problems, and the DelAC actor-critic MARL algorithm outperforms existing methods in simulations.
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Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning
MARS replaces additive clipping and soft penalties in multi-agent trust-region methods with a symmetric geometric barrier, matching or exceeding MAPPO and MASPO performance across 47 tasks in eight environments.
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SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
SACHI uses graph transformer convolutions on inter-agent coordination graphs to enrich partial-observation agents with content-dependent teammate information, yielding statistically significant gains over baselines in...
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SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
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SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems
SOAR is a unified DRL method using soft allocations, event-driven MDP, and heterogeneous graph transformers that cuts global makespan by 7.5% and average order completion time by 15.4% at sub-100ms latency in RMFS.
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Scalable Neighborhood-Based Multi-Agent Actor-Critic
MADDPG-K scales centralized critics in multi-agent RL by limiting each critic to k-nearest neighbors under Euclidean distance, yielding constant input size and competitive performance.
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and ...
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AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning
AdaFair-MARL enforces workload fairness as an explicit second-order cone constraint in cooperative MARL via adaptive primal-dual optimization, achieving near-perfect constraint satisfaction while preserving team performance.
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Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning
Optimistic ε-Greedy Exploration adds decoupled optimistic networks that converge in probability to maximum returns and samples from them with probability ε to increase optimal joint-action frequency in CTDE MARL.
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From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
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Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies
CoSER adaptively samples joint actions in CTDE MARL to reduce sampling error relative to the joint on-policy distribution, empirically improving reliability of independent policy gradient convergence.
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Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning
Proposes a clipping objective for sequential trust-region updates in independent-actor cooperative MARL that yields a monotonic improvement bound and sub-linear convergence to epsilon-Nash equilibria while reducing ad...
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