4.3k citations · 4.9k across the 11 of their papers we have counts for
4 papers · 1 filter
Quantifying Generalization in Reinforcement Learning
Karl Cobbe, Oleg Klimov, Chris Hesse +2
In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for bo…
Model-Based Reinforcement Learning via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman +3
Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-wor…
Gotta Learn Fast: A New Benchmark for Generalization in RL
Alex Nichol, Vicki Pfau, Christopher Hesse +2
In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performa…
On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, John Schulman
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented w…