49 citations · 56 across the 2 of their papers we have counts for
3 papers
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…
Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov +1
We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework which modifies traditional on-policy actor-critic methods by separating policy and value function trai…
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton +1
We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learnin…