From the 1 of 5 linked papers with an AI index.
5 papers
Fully Offline Reinforcement Learning
Mattie Fellows, Clarisse Wibault, Uljad Berdica +3
The paper proposes fully offline reinforcement learning methods that use Bayesian model-based techniques to learn dynamics and evaluate policies without any online interaction, ena…
Inverting the Bellman Equation: From -Values to World Models
Alistair Letcher, Mattie Fellows, Alexander D. Goldie +3
Model-based and model-free reinforcement learning are traditionally viewed as separate paradigms: instead of learning a model of the transition kernel , model-free agents typica…
Evolution Strategies at the Hyperscale
Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17
Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naï…
Simplifying Deep Temporal Difference Learning
Matteo Gallici, Mattie Fellows, Benjamin Ellis +4
Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation…
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
Benjamin Ellis, Matthew T. Jackson, Andrei Lupu +4
In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. Howev…