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AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research
The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research.
AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection
In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW.
Towards Practical Credit Assignment for Deep Reinforcement Learning
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards.
QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error
We implement the objective with an adversarial Q-learning method in which Q and Qx are the action-value functions for extrinsic and secondary rewards, respectively.
Reward Prediction Error as an Exploration Objective in Deep RL
We then propose a deep reinforcement learning method, QXplore, which exploits the temporal difference error of a Q-function to solve hard exploration tasks in high-dimensional MDPs.
Q-Learning for Continuous Actions with Cross-Entropy Guided Policies
CGP aims to combine the stability and performance of iterative sampling policies with the low computational cost of a policy network.
Program Synthesis Through Reinforcement Learning Guided Tree Search
Program Synthesis is the task of generating a program from a provided specification.
