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Found 7 papers, 2 papers with code
SMX: Sequential Monte Carlo Planning for Expert Iteration
SMX demonstrates a statistically significant improvement in performance compared to AlphaZero, as well as demonstrating its performance as an improvement operator for a model-free policy, matching or exceeding top model-free methods across both continuous and discrete environments.
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.
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.
The challenge of redundancy on multi-agent value factorisation
In the field of cooperative multi-agent reinforcement learning (MARL), the standard paradigm is the use of centralised training and decentralised execution where a central critic conditions the policies of the cooperative agents based on a central state.
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.
