49 citations · 58 across the 2 of their papers we have counts for
6 papers
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
Ingmar Kanitscheider, Joost Huizinga, David Farhi +9
An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks. If tasks depend on each other (e.g. needing to learn to walk before lear…
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…
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…
Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey +1
We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error o…
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…
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal +2
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "sur…