93 citations · 99 across the 10 of their papers we have counts for
6 papers · 1 filter
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother +3
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversaria…
CALE: Continuous Arcade Learning Environment
Jesse Farebrother, Pablo Samuel Castro
We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013]. The CALE uses the same un…
Foundations of Multivariate Distributional Reinforcement Learning
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +1
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and repr…
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother, Jordi Orbay, Quan Vuong +9
Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression ob…
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Johan Obando-Ceron, Ghada Sokar, Timon Willi +6
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analog…
A Distributional Analogue to the Successor Representation
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +5
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Anal…