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Found 3 papers, 1 papers with code
A study on the plasticity of neural networks
One aim shared by multiple settings, such as continual learning or transfer learning, is to leverage previously acquired knowledge to converge faster on the current task.
Multi-task Deep Reinforcement Learning with PopArt
This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on.
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Mix & Match - Agent Curricula for Reinforcement Learning
We introduce Mix and match (M&M) – a training framework designed to facilitate rapid and effective learning in RL agents that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents.
