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Felix Schmitt

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4 papers
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4

AAAI Conference 2024 Conference Paper

Reward (Mis)design for Autonomous Driving (Abstract Reprint)

  • W. Bradley Knox
  • Alessandro Allievi
  • Holger Banzhaf
  • Felix Schmitt
  • Peter Stone

This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaws in reward functions. We survey research that is published in top-tier venues and focuses on reinforcement learning (RL) for autonomous driving (AD). Specifically, we closely examine the reported reward function in each publication and present these reward functions in a complete and standardized format in the appendix. Wherever we have sufficient information, we apply the 8 sanity checks to each surveyed reward function, revealing near-universal flaws in reward design for AD that might also exist pervasively across reward design for other tasks. Lastly, we explore promising directions that may aid the design of reward functions for AD in subsequent research, following a process of inquiry that can be adapted to other domains.

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AIJ Journal 2023 Journal Article

Reward (Mis)design for autonomous driving

  • W. Bradley Knox
  • Alessandro Allievi
  • Holger Banzhaf
  • Felix Schmitt
  • Peter Stone

This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaws in reward functions. We survey research that is published in top-tier venues and focuses on reinforcement learning (RL) for autonomous driving (AD). Specifically, we closely examine the reported reward function in each publication and present these reward functions in a complete and standardized format in the appendix. Wherever we have sufficient information, we apply the 8 sanity checks to each surveyed reward function, revealing near-universal flaws in reward design for AD that might also exist pervasively across reward design for other tasks. Lastly, we explore promising directions that may aid the design of reward functions for AD in subsequent research, following a process of inquiry that can be adapted to other domains.

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IJCAI Conference 2022 Conference Paper

Value Refinement Network (VRN)

  • Jan Wöhlke
  • Felix Schmitt
  • Herke van Hoof

In robotic tasks, we encounter the unique strengths of (1) reinforcement learning (RL) that can handle high-dimensional observations as well as unknown, complex dynamics and (2) planning that can handle sparse and delayed rewards given a dynamics model. Combining these strengths of RL and planning, we propose the Value Refinement Network (VRN), in this work. Our VRN is an RL-trained neural network architecture that learns to locally refine an initial (value-based) plan in a simplified (2D) problem abstraction based on detailed local sensory observations. We evaluate the VRN on simulated robotic (navigation) tasks and demonstrate that it can successfully refine sub-optimal plans to match the performance of more costly planning in the non-simplified problem. Furthermore, in a dynamic environment, the VRN still enables high task completion without global re-planning.

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AAAI Conference 2017 Conference Paper

I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior

  • Felix Schmitt
  • Hans-Joachim Bieg
  • Michael Herman
  • Constantin Rothkopf

Human motor behavior is naturally guided by sensing the environment. To predict such sensori-motor behavior, it is necessary to model what is sensed and how actions are chosen based on the obtained sensory measurements. Although several models of human sensing haven been proposed, rarely data of the assumed sensory measurements is available. This makes statistical estimation of sensor models problematic. To overcome this issue, we propose an abstract structural estimation approach building on the ideas of Herman et al. ’s Simultaneous Estimation of Rewards and Dynamics (SERD). Assuming optimal fusion of sensory information and rational choice of actions the proposed method allows to infer sensor models even in absence of data of the sensory measurements. To the best of our knowledge, this work presents the first general approach for joint inference of sensor and policy models. Furthermore, we consider its concrete implementation in the important class of sensor scheduling linear quadratic Gaussian problems. Finally, the effectiveness of the approach is demonstrated for prediction of the behavior of automobile drivers. Specifically, we model the glance and steering behavior of driving in the presence of visually demanding secondary tasks. The results show, that prediction benefits from the inference of sensor models. This is the case, especially, if also information is considered, that is contained in gaze switching behavior.

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