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03/07/2023
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
Model-based reinforcement learning is one approach to increase sample ef...
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03/28/2022
Revisiting Model-based Value Expansion
Model-based value expansion methods promise to improve the quality of va...
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10/24/2021
A Differentiable Newton-Euler Algorithm for Real-World Robotics
Obtaining dynamics models is essential for robotics to achieve accurate ...
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10/05/2021
Continuous-Time Fitted Value Iteration for Robust Policies
Solving the Hamilton-Jacobi-Bellman equation is important in many domain...
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10/05/2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
Deep learning has been widely used within learning algorithms for roboti...
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09/29/2021
Learning Dynamics Models for Model Predictive Agents
Model-Based Reinforcement Learning involves learning a dynamics model fr...
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05/25/2021
Robust Value Iteration for Continuous Control Tasks
When transferring a control policy from simulation to a physical system,...
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05/10/2021
Value Iteration in Continuous Actions, States and Time
Classical value iteration approaches are not applicable to environments ...
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11/03/2020
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
A limitation of model-based reinforcement learning (MBRL) is the exploit...
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10/26/2020
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
Robots that can learn in the physical world will be important to en-able...
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10/19/2020
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
In this work, we examine a spectrum of hybrid model for the domain of mu...
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09/13/2019
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints
Learning optimal feedback control laws capable of executing optimal traj...
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07/10/2019
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
Deep learning has achieved astonishing results on many tasks with large ...
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07/10/2019