Accelerating Reinforcement Learning through GPU Atari Emulation

By Steven Dalton, Iuri Frosio. 2019

48 citations recorded by the source corpus.

Subfields: Reinforcement Learning, ML Systems & Hardware

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Dhruv Batra
2. Erik Wijmans
3. Devendra Singh Chaplot
4. Manolis Savva
5. Jitendra Malik
6. Vladlen Koltun
7. Sergey Levine
8. F. Meier

In ML Systems & Hardware

1. Vladlen Koltun
2. Dhruv Batra
3. Brennan Shacklett

Citations in the corpus

• Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “[75] Steven Dalton, Iuri Frosio, and Michael Garland.”
• Gymnasium: A Standard Interface for Reinforcement Learning Environments (2025, unknown): “Observation space”
• Large Batch Simulation for Deep Reinforcement Learning (2021, methodology): “Concurrently with our work, CuLE, a GPU-accelerated reimplementation of the Atari Learning Environment (ALE), demonstrates the benefits of centralized batch simulation (Dalton et al., 2020).”