Search Results for author:
Found 12 papers, 7 papers with code
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference
We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL).
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
And, how close are we to general visual representations?
Ranked #10 on
Image Classification
on VTAB-1k
(using extra training data)
The Visual Task Adaptation Benchmark
Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.
The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
FVD: A new Metric for Video Generation
While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.
Towards Accurate Generative Models of Video: A New Metric & Challenges
To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.
A Large-Scale Study on Regularization and Normalization in GANs
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
Are GANs Created Equal? A Large-Scale Study
Generative adversarial networks (GAN) are a powerful subclass of generative models.
