9 citations · 25 across the 6 of their papers we have counts for
10 papers
imitation: Clean Imitation Learning Implementations
Adam Gleave, Mohammad Taufeeque, Juan Rocamonde +7
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imita…
Preprocessing Reward Functions for Interpretability
Erik Jenner, Adam Gleave
In many real-world applications, the reward function is too complex to be manually specified. In such cases, reward functions must instead be learned from human feedback. Since the…
A Primer on Maximum Causal Entropy Inverse Reinforcement Learning
Adam Gleave, Sam Toyer
Inverse Reinforcement Learning (IRL) algorithms infer a reward function that explains demonstrations provided by an expert acting in the environment. Maximum Causal Entropy (MCE) I…
Uncertainty Estimation for Language Reward Models
Adam Gleave, Geoffrey Irving
Language models can learn a range of capabilities from unsupervised training on text corpora. However, to solve a particular problem (such as text summarization) it is typically ne…
Understanding Learned Reward Functions
Eric J. Michaud, Adam Gleave, Stuart Russell
In many real-world tasks, it is not possible to procedurally specify an RL agent's reward function. In such cases, a reward function must instead be learned from interacting with a…
DERAIL: Diagnostic Environments for Reward And Imitation Learning
Pedro Freire, Adam Gleave, Sam Toyer +1
The objective of many real-world tasks is complex and difficult to procedurally specify. This makes it necessary to use reward or imitation learning algorithms to infer a reward or…