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RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes
The high-speed off-road driving task represents a particularly challenging instantiation of this problem: a high-return policy should drive as aggressively and as quickly as possible, which often requires getting close to the edge of the set of "safe" states, and therefore places a particular burden on the method to avoid frequent failures.
SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation
Specifically, SELFI stabilizes the online learning process by incorporating the same model-based learning objective from offline pre-training into the Q-values learned with online model-free reinforcement learning.
ViNT: A Foundation Model for Visual Navigation
In this paper, we describe the Visual Navigation Transformer (ViNT), a foundation model that aims to bring the success of general-purpose pre-trained models to vision-based robotic navigation.
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL).
