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Found 10 papers, 5 papers with code
Neural Regression For Scale-Varying Targets
In this work, we demonstrate that a major limitation of regression using a mean-squared error loss is its sensitivity to the scale of its targets.
When does return-conditioned supervised learning work for offline reinforcement learning?
Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL).
Non-Markovian policies occupancy measures
A central object of study in Reinforcement Learning (RL) is the Markovian policy, in which an agent's actions are chosen from a memoryless probability distribution, conditioned only on its current state.
The Importance of Pessimism in Fixed-Dataset Policy Optimization
To avoid this, algorithms can follow the pessimism principle, which states that we should choose the policy which acts optimally in the worst possible world.
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
We show that the optimization of these objectives guarantees (1) the quality of the latent space as a representation of the state space and (2) the quality of the DeepMDP as a model of the environment.
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity.
Neural Lattice Language Models
In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models.
Is Generator Conditioning Causally Related to GAN Performance?
Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs).
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
It is well known that it is possible to construct "adversarial examples" for neural networks: inputs which are misclassified by the network yet indistinguishable from true data.
