Summary One Big Net For Everything Technical Report arxiv.org
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The report investigates the effectiveness of ONE, a single recurrent neural network, in solving problems and learning new tasks without forgetting previous skills.
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Key Points
- ONE is a single recurrent neural network (RNN) that can continually learn new tasks without forgetting previous skills.
- ONE can be trained using black box optimization, reinforcement learning, and artificial intelligence techniques.
- ONE can predict future inputs based on previous inputs and actions, and can be trained using all input-output traces from past trials.
- Incremental learning in ONE involves absorbing control and prediction skills through gradient-based compression of desirable behaviors.
- The importance of retaining all data from previous experiences is emphasized.
- The input text includes a list of references and citations related to artificial neural networks, machine learning, and artificial intelligence.
- The reports cover various topics such as neural networks, reinforcement learning, and artificial intelligence.
Summaries
25 word summary
This report explores the use of a single recurrent neural network (RNN) called ONE to solve problems and learn new tasks without forgetting previous skills.
43 word summary
This technical report discusses the concept of using a single recurrent neural network (RNN) called ONE as a problem solver that can continually learn new tasks without forgetting previous skills. ONE is trained in various ways, including black box optimization, reinforcement learning, and
423 word summary
This technical report discusses the concept of using a single recurrent neural network (RNN) called ONE as a problem solver that can continually learn new tasks without forgetting previous skills. ONE is trained in various ways, including black box optimization, reinforcement learning, artificial
Reinforcement learning RNN controllers can benefit from using gradient-based RNNs as predictive world models. The document discusses the incremental training of a problem solver called ONE, which is a single RNN. ONE is trained in various ways, including black
The document discusses the use of ONE, a machine learning device, for predicting future inputs based on previous inputs and actions. To improve its predictive abilities, ONE can be trained using all input-output traces from past trials, including failed ones. After learning a
In the document "One Big Net For Everything Technical Report," the authors discuss the training of copies of ONE (a neural network) through a black box optimization method. This is done through incremental neuroevolution, hierarchical neuroevolution, hierarchical policy gradient
In certain applications where storage space is limited, it may be necessary to store and re-train on only low-resolution versions of previous observations. This can be achieved by encoding limited previous experiences into the algorithm to provide information for new tasks. By compressing
In this technical report, the authors propose a method for incremental learning in a recurrent neural network called ONE. The authors explain that ONE can absorb control and prediction skills through gradient-based compression of desirable behaviors. They emphasize the importance of retaining all data from previous
This excerpt is a list of references cited in a technical report. The references include various sources such as PhD theses, journal articles, conference proceedings, and preprints. Some of the topics covered in these references include policy-gradient algorithms, unsupervised
This excerpt includes a list of references to various publications related to artificial neural networks and machine learning. The references cover a range of topics, including pattern recognition, LSTM (Long Short-Term Memory), hierarchical policy gradient algorithms, natural evolution strategies, formal und
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This document is a list of technical reports and articles written by Jürgen Schmidhuber. The reports cover a range of topics including neural networks, reinforcement learning, and artificial intelligence. Some of the key points highlighted in the reports include the
This document is a list of references and citations for various papers and articles related to the field of artificial intelligence and machine learning. The references cover a wide range of topics including neural networks, evolutionary algorithms, reinforcement learning, and natural language processing. Some notable