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Found 6 papers, 2 papers with code
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.
Reduce, Reuse, Recycle: Selective Reincarnation in Multi-Agent Reinforcement Learning
'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment.
On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa
In this paper, we compare this random sampling approach to more advanced pseudo-absence generation methods, such as environmental profiling and optimal background extent limitation, specifically for predicting desert locust breeding grounds in Africa.
Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Based upon these findings, we show that gradient flow in sparse networks can be improved by reconsidering aspects of the architecture design and the training regime.
