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Found 17 papers, 4 papers with code
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics
Specifically, control of wave dynamics is challenging due to additional physical constraints and intrinsic properties of wave phenomena such as dissipation, attenuation, reflection, and scattering.
Controllability-Constrained Deep Network Models for Enhanced Control of Dynamical Systems
That is achieved by augmenting the model estimation objective with a controllability constraint, which penalizes models with a low degree of controllability.
Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems
While recommender systems have become an integral component of the Web experience, their heavy reliance on user data raises privacy and security concerns.
Bounding the Optimal Value Function in Compositional Reinforcement Learning
In order to quickly obtain solutions to unseen problems with new reward functions, a popular approach involves functional composition of previously solved tasks.
Compositionality and Bounds for Optimal Value Functions in Reinforcement Learning
An agent's ability to reuse solutions to previously solved problems is critical for learning new tasks efficiently.
Intrinsic Motivation in Dynamical Control Systems
Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation.
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning
In this work, we develop a general framework for reward shaping and task composition in entropy-regularized RL.
Multi-Objective Policy Gradients with Topological Constraints
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a curriculum, and enforcing measures of safety.
Entropy Regularized Reinforcement Learning Using Large Deviation Theory
The mapping established in this work connects current research in reinforcement learning and non-equilibrium statistical mechanics, thereby opening new avenues for the application of analytical and computational approaches from one field to cutting-edge problems in the other.
GEM: Group Enhanced Model for Learning Dynamical Control Systems
In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning.
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle to adapt to new, unseen environments.
Efficient Empowerment Estimation for Unsupervised Stabilization
We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches.
AvE: Assistance via Empowerment
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s).
Preventing Imitation Learning with Adversarial Policy Ensembles
To our knowledge, this is the first work regarding the protection of policies in Reinforcement Learning.
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards
While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge.
Learning Efficient Representation for Intrinsic Motivation
The core idea is to represent the relation between action sequences and future states using a stochastic dynamic model in latent space with a specific form.
Dynamical System Embedding for Efficient Intrinsically Motivated Artificial Agents
In this work, we develop a novel approach for the estimation of empowerment in unknown arbitrary dynamics from visual stimulus only, without sampling for the estimation of MIAS.
