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Found 4 papers, 1 papers with code
Policy composition in reinforcement learning via multi-objective policy optimization
In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
Augmenting learning using symmetry in a biologically-inspired domain
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations.
Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data.
Soft Condensed Matter
