22 citations · 24 across the 3 of their papers we have counts for
4 papers · 1 filter
The Benefits of Power Regularization in Cooperative Reinforcement Learning
Michelle Li, Michael Dennis
Cooperative Multi-Agent Reinforcement Learning (MARL) algorithms, trained only to optimize task reward, can lead to a concentration of power where the failure or adversarial intent…
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky +4
A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of task…
Quantifying Differences in Reward Functions
Adam Gleave, Michael Dennis, Shane Legg +2
For many tasks, the reward function is inaccessible to introspection or too complex to be specified procedurally, and must instead be learned from user data. Prior work has evaluat…
Adversarial Policies: Attacking Deep Reinforcement Learning
Adam Gleave, Michael Dennis, Cody Wild +3
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, a…