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Exploring through Random Curiosity with General Value Functions
Aditya Ramesh · Louis Kirsch · Sjoerd van Steenkiste · Jürgen Schmidhuber
Event URL: https://openreview.net/forum?id=-TQEKIFvzAb »

Exploration in reinforcement learning through intrinsic rewards has previously been addressed by approaches based on state novelty or artificial curiosity. In partially observable settings where observations look alike, state novelty can lead to intrinsic reward vanishing prematurely. On the other hand, curiosity-based approaches require modeling precise environment dynamics which are potentially quite complex. Here we propose random curiosity with general value functions (RC-GVF), an intrinsic reward function that connects state novelty and artificial curiosity. Instead of predicting the entire environment dynamics, RC-GVF predicts temporally extended values through general value functions (GVFs) and uses the prediction error as an intrinsic reward. In this way, our approach generalizes a popular approach called random network distillation (RND) by encouraging behavioral diversity and reduces the need for additional maximum entropy regularization. Our experiments on four procedurally generated partially observable environments indicate that our approach is competitive to RND and could be beneficial in environments that require behavioural exploration.

Author Information

Aditya Ramesh (IDSIA)
Louis Kirsch (The Swiss AI Lab IDSIA)
Sjoerd van Steenkiste (Google Research)
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI); NNAISENSE; KAUST)

Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By the mid 2010s, they were on 3 billion devices, and used billions of times per day through users of the world's most valuable public companies, e.g., for greatly improved (CTC-LSTM-based) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTM-based translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). All of this attracted enormous interest from industry. His research group also established the fields of mathematically rigorous universal AI and recursive self-improvement in metalearning machines that learn to learn (since 1987). In 1990, he introduced unsupervised adversarial neural networks that fight each other in a minimax game to achieve artificial curiosity (GANs are a special case). In 1991, he introduced very deep learning through unsupervised pre-training, and neural fast weight programmers formally equivalent to what's now called linear Transformers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. He is recipient of numerous awards, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is a frequent keynote speaker, and advising various governments on AI strategies.

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