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Found 6 papers, 1 papers with code
A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7).
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Recent advances in CV and NLP have been largely driven by scaling up the number of network parameters, despite traditional theories suggesting that larger networks are prone to overfitting.
A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics.
Composing Efficient, Robust Tests for Policy Selection
Empirical results demonstrate that RPOSST finds a small set of test cases that identify high quality policies in a toy one-shot game, poker datasets, and a high-fidelity racing simulator.
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
These value estimates provide insight into an agent's learning and decision-making process and enable new training methods to mitigate common problems.
Analysis and Observations from the First Amazon Picking Challenge
This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.
Robotics
