Search Results for author:
Found 7 papers, 4 papers with code
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Observing this discrepancy, in this paper, we investigate whether the scalability of deep RL can also be improved simply by using classification in place of regression for training value functions.
Mixtures of Experts Unlock Parameter Scaling for Deep RL
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.
A Distributional Analogue to the Successor Representation
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process.
Distributional Reinforcement Learning
Model-based Reinforcement Learning
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Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM).
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Combined with a suitable off-policy learning rule, the result is a representation learning algorithm that can be understood as extending Mahadevan & Maggioni (2007)'s proto-value functions to deep reinforcement learning -- accordingly, we call the resulting object proto-value networks.
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
In this paper, we derive an algorithm that learns a principal subspace from sample entries, can be applied when the approximate subspace is represented by a neural network, and hence can be scaled to datasets with an effectively infinite number of rows and columns.
Generalization and Regularization in DQN
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks.
