Michael Lutter | DeepAI

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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...

0 Daniel Palenicek, et al. ∙

∙ 03/28/2022

Revisiting Model-based Value Expansion

Model-based value expansion methods promise to improve the quality of va...

0 Daniel Palenicek, et al. ∙

∙ 10/24/2021

A Differentiable Newton-Euler Algorithm for Real-World Robotics

Obtaining dynamics models is essential for robotics to achieve accurate ...

0 Michael Lutter, et al. ∙

∙ 10/05/2021

Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domain...

1 Michael Lutter, et al. ∙

∙ 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...

0 Michael Lutter, et al. ∙

∙ 09/29/2021

Learning Dynamics Models for Model Predictive Agents

Model-Based Reinforcement Learning involves learning a dynamics model fr...

0 Michael Lutter, et al. ∙

∙ 05/25/2021

Robust Value Iteration for Continuous Control Tasks

When transferring a control policy from simulation to a physical system,...

2 Michael Lutter, et al. ∙

∙ 05/10/2021

Value Iteration in Continuous Actions, States and Time

Classical value iteration approaches are not applicable to environments ...

4 Michael Lutter, et al. ∙

∙ 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...

0 Michael Lutter, et al. ∙

∙ 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...

0 Kai Ploeger, et al. ∙

∙ 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...

0 Michael Lutter, et al. ∙

∙ 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...

0 Michael Lutter, et al. ∙

∙ 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 ...

0 Michael Lutter, et al. ∙

∙ 07/10/2019

Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

Applying Deep Learning to control has a lot of potential for enabling th...

0 Michael Lutter, et al. ∙