Search Results for author:
Found 8 papers, 6 papers with code
Stochastic Variational Inference
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions.
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time.
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.
An Empirical Study of Implicit Regularization in Deep Offline RL
Also, we empirically identify three phases of learning that explain the impact of implicit regularization on the learning dynamics and found that bootstrapping alone is insufficient to explain the collapse of the effective rank.
