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> Reinforcement learning agents typically perform poorly if provided with inputs that were not clearly defined in training. A new approach enables RL agents to
by LudwigNagasena 5y ago
> Reinforcement learning agents typically perform poorly if provided with inputs that were not clearly defined in training. A new approach enables RL agents to perform well, even when subject to corrupt, incomplete, or shuffled inputs.
I am not a specialist in NN and the article doesn’t even mention it, but shouldn’t input dropout and input corruption help with this?
- brrrrrm 5y agoFrom a description in the referenced NeurIPs spotlight paper (https://attentionneuron.github.io https://attentionneuron.github.io) > While it is possible to acquire a quasi-PI agent by training with randomly shuffled observations and hope the agent’s policy network has enough capacity to memorize all the patterns, we aim for a design that achieves true PI even if the agent is trained with fix-ordered observations.