55 citations · 56 across the 3 of their papers we have counts for
3 papers
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO
Skander Moalla, Andrea Miele, Daniil Pyatko +2
Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, netwo…
An Empirical Study of Implicit Regularization in Deep Offline RL
Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski +5
Deep neural networks are the most commonly used function approximators in offline reinforcement learning. Prior works have shown that neural nets trained with TD-learning and gradi…
Recurrent Batch Normalization
Tim Cooijmans, Nicolas Ballas, César Laurent +2
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the i…