82 citations · 105 across the 8 of their papers we have counts for
6 papers · 1 filter
Revisiting Model-based Value Expansion
Daniel Palenicek, Michael Lutter, Jan Peters
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the effectiveness of value function learning. However, to date, these met…
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
Robust Value Iteration for Continuous Control Tasks
Michael Lutter, Shie Mannor, Jan Peters +2
When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. Commonly, the optimal policy o…
Value Iteration in Continuous Actions, States and Time
Michael Lutter, Shie Mannor, Jan Peters +2
Classical value iteration approaches are not applicable to environments with continuous states and actions. For such environments, the states and actions are usually discretized, w…
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints
Michael Lutter, Boris Belousov, Kim Listmann +2
Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learni…
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
Michael Lutter, Christian Ritter, Jan Peters
Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data. For many important real-world appl…