Permutation-Invariant Neural Networks for Reinforcement Learning
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.
Note: This…