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Found 7 papers, 3 papers with code
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL
Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges.
Efficiently Quantifying Individual Agent Importance in Cooperative MARL
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL).
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning
Establishing sound experimental standards and rigour is important in any growing field of research.
Generalisable Agents for Neural Network Optimisation
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times.
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
Combining these recommendations, with novel insights from our analysis, we propose a standardised performance evaluation protocol for cooperative MARL.
Mava: a research library for distributed multi-agent reinforcement learning in JAX
Our criteria for such software is that it should be simple enough to use to implement new ideas quickly, while at the same time be scalable and fast enough to test those ideas in a reasonable amount of time.
