Search Results for author:
Found 8 papers, 4 papers with code
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.
Do deep reinforcement learning agents model intentions?
In this work we test whether deep reinforcement learning agents explicitly represent other agents' intentions (their specific aims or goals) during a task in which the agents had to coordinate the covering of different spots in a 2D environment.
Teacher-Student Curriculum Learning
We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on.
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.
