121 citations · 215 across the 18 of their papers we have counts for
9 papers · 1 filter
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister +2
Causal confusion is a phenomenon where an agent learns a policy that reflects imperfect spurious correlations in the data. Such a policy may falsely appear to be optimal during tra…
Control-Aware Prediction Objectives for Autonomous Driving
Rowan McAllister, Blake Wulfe, Jean Mercat +3
Autonomous vehicle software is typically structured as a modular pipeline of individual components (e.g., perception, prediction, and planning) to help separate concerns into inter…
Dynamics-Aware Comparison of Learned Reward Functions
Blake Wulfe, Ashwin Balakrishna, Logan Ellis +3
The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. However, comparing reward functions, for example a…
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama +7
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the…
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama +6
Traffic simulators are important tools in autonomous driving development. While continuous progress has been made to provide developers more options for modeling various traffic pa…