Search Results for author:
Found 7 papers, 2 papers with code
Combating the effects of speed and delays in end-to-end self-driving
We experimentally show that models trained to drive fast cannot perform the seemingly easier task of driving slow and vice-versa.
Controlling Steering with Energy-Based Models
So-called implicit behavioral cloning with energy-based models has shown promising results in robotic manipulation tasks.
LiDAR-as-Camera for End-to-End Driving
The core task of any autonomous driving system is to transform sensory inputs into driving commands.
A Survey of End-to-End Driving: Architectures and Training Methods
Autonomous driving is of great interest to industry and academia alike.
Perspective Taking in Deep Reinforcement Learning Agents
We believe that, in the long run, building better artificial agents with perspective taking ability can help us develop artificial intelligence that is more human-like and easier to communicate with.
APES: a Python toolbox for simulating reinforcement learning environments
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years.
Multiagent Cooperation and Competition with Deep Reinforcement Learning
In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.
