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Found 5 papers, 1 papers with code
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
To this end, we introduce a neural network architecture, the Decision Adapter, which generates the weights of an adapter module and conditions the behaviour of an agent on the context information.
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
The agent can then flexibly compose them both logically and temporally to provably achieve temporal logic specifications in any regular language, such as regular fragments of linear temporal logic.
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning
In this work, we investigate the properties of data that cause popular representation learning approaches to fail.
The Role of Learning Regime, Architecture and Dataset Structure on Systematic Generalization in Simple Neural Networks
We introduce a minimal space of datasets with systematic and non-systematic features in both the input and output.
Using Objective Bayesian Methods to Determine the Optimal Degree of Curvature within the Loss Landscape
The efficacy of the width of the basin of attraction surrounding a minimum in parameter space as an indicator for the generalizability of a model parametrization is a point of contention surrounding the training of artificial neural networks, with the dominant view being that wider areas in the landscape reflect better generalizability by the trained model.
