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>Just let the network train itself without having a human laboriously determine the topology of the neural network I hope this little koan illustrates that thi

>Just let the network train itself without having a human laboriously determine the topology of the neural network I hope this little koan illustrates that this sentence is impossible to execute. The human always has to specify something. -- In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. "What are you doing?", asked Minsky. "I am training a randomly wired neural net to play Tic-tac-toe", Sussman replied. "Why is the net wired randomly?", asked Minsky. "I do not want it to have any preconceptions of how to play", Sussman said. Minsky then shut his eyes. "Why do you close your eyes?" Sussman asked his teacher. "So that the room will be empty." At that moment, Sussman was enlightened.

king07828 8y ago
Agreed that without any constraints, it could become a Sisyphean task. The exercise then becomes one of finding the minimal constraints needed to achieve the desired results. Please correct if needed, but looking at the Dota 2 neural network [1], it boils down to generating an input State vector from the Dota 2 bot output interface, running the state Vector through an lstm (of sufficient length) to generate an output State vector, and generating the inputs for the Dota 2 bot input interface from the output State vector. Update this network (1) to have the input State Vector generated from a convolutional network that feeds a fully connected Network and uses the frame buffer as input and (2) to have the final outputs of the neural network be keyboard and mouse commands instead of dota 2 bot input interface commands, then let the network train itself. The number of elements in the state vector, the number of convolutional layers, the number of lstm layers, and the number of layers and elements in each fully connected hidden layer could each also be determined by a recurrent neural network. [1] https://towardsdatascience.com/the-science-behind-openai-five-that-just-produced-one-of-the-greatest-breakthrough-in-the-history-b045bcdc2b69 https://towardsdatascience.com/the-science-behind-openai-fiv... (see the image under "The Architecture") [ random capitalization powered by Google speech dictation ]

king07828 8y ago

Agreed that without any constraints, it could become a Sisyphean task. The exercise then becomes one of finding the minimal constraints needed to achieve the desired results. Please correct if needed, but looking at the Dota 2 neural network [1], it boils down to generating an input State vector from the Dota 2 bot output interface, running the state Vector through an lstm (of sufficient length) to generate an output State vector, and generating the inputs for the Dota 2 bot input interface from the output State vector. Update this network (1) to have the input State Vector generated from a convolutional network that feeds a fully connected Network and uses the frame buffer as input and (2) to have the final outputs of the neural network be keyboard and mouse commands instead of dota 2 bot input interface commands, then let the network train itself. The number of elements in the state vector, the number of convolutional layers, the number of lstm layers, and the number of layers and elements in each fully connected hidden layer could each also be determined by a recurrent neural network. [1] https://towardsdatascience.com/the-science-behind-openai-five-that-just-produced-one-of-the-greatest-breakthrough-in-the-history-b045bcdc2b69 https://towardsdatascience.com/the-science-behind-openai-fiv... (see the image under "The Architecture") [ random capitalization powered by Google speech dictation ]