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Found 6 papers, 4 papers with code
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting
This re-weighted sampling strategy may be combined with any offline RL algorithm.
A single gradient step finds adversarial examples on random two-layers neural networks
Daniely and Schacham recently showed that gradient descent finds adversarial examples on random undercomplete two-layers ReLU neural networks.
Adversarial score matching and improved sampling for image generation
Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling.
Ranked #54 on
Image Generation
on CIFAR-10
Safe Policy Improvement with an Estimated Baseline Policy
Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with Baseline Bootstrapping (SPIBB) fix: reproduce the baseline policy in the uncertain state-action pairs, in order to control the variance on the trained policy performance.
Safe Policy Improvement with Soft Baseline Bootstrapping
Batch Reinforcement Learning (Batch RL) consists in training a policy using trajectories collected with another policy, called the behavioural policy.
Safe Policy Improvement with Baseline Bootstrapping
Finally, we implement a model-free version of SPIBB and show its benefits on a navigation task with deep RL implementation called SPIBB-DQN, which is, to the best of our knowledge, the first RL algorithm relying on a neural network representation able to train efficiently and reliably from batch data, without any interaction with the environment.
