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Found 6 papers, 3 papers with code
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.
Revisiting the Gumbel-Softmax in MADDPG
This method, however, is statistically biased, and a recent MARL benchmarking paper suggests that this bias makes MADDPG perform poorly in grid-world situations, where the action space is discrete.
Reinforcement Learning for Economic Policy: A New Frontier?
Agent-based computational economics is a field with a rich academic history, yet one which has struggled to enter mainstream policy design toolboxes, plagued by the challenges associated with representing a complex and dynamic reality.
BaIT: Barometer for Information Trustworthiness
This paper presents a new approach to the FNC-1 fake news classification task which involves employing pre-trained encoder models from similar NLP tasks, namely sentence similarity and natural language inference, and two neural network architectures using this approach are proposed.
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.
