1.5k citations · 1.6k across the 4 of their papers we have counts for
6 papers · 1 filter
AutoDIME: Automatic Design of Interesting Multi-Agent Environments
Ingmar Kanitscheider, Harri Edwards
Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the di…
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Alethea Power, Yuri Burda, Harri Edwards +2
In this paper we propose to study generalization of neural networks on small algorithmically generated datasets. In this setting, questions about data efficiency, memorization, gen…
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun +55
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…
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…
How to train your MAML
Antreas Antoniou, Harrison Edwards, Amos Storkey
The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic…
Large-Scale Study of Curiosity-Driven Learning
Yuri Burda, Harri Edwards, Deepak Pathak +3
Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense…