In this paper, we propose a heterogeneous relational deep reinforcement learning method, named HeR-DRL, which utilizes a customized heterogeneous Graph Neural Network (GNN) to enhance overall ...
Abstract: Leveraging the power of deep reinforcement learning (DRL) and strategic knowledge transfer, our study introduces PIRA-DRL-DTRL, a novel approach to optimizing resource allocation in emerging ...
ROS (Popular & Comprehensive physical simulator for robots; Heavy and Slow): Webots (Popular physical simulator for robots; Faster than ROS; Less realistic): DQN: Mnih V, Kavukcuoglu K, Silver D, et ...
This is a recent upload of a project from 16 months ago. The code is somewhat poorly written, lacking in extensibility and likely not maintainable. It fails to pass some static analysis and has shown ...
As artificial intelligence continues to evolve at an unprecedented pace, a new organization has emerged to address one of the most profound and complex questions of our time: Can machines become ...
Having machines learn from experience was once considered a dead end. It’s now critical to artificial intelligence, and work ...
Two trailblazing computer scientists have won the 2024 Turing Award for their work in reinforcement learning, a discipline in which machines learn through a reward-based trial-and-error approach that ...
OpenAI warns AI labs about the risks of controlling AI thought processes, highlighting dangers like obfuscation and reward ...
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