Rare hang

[HyperCodec]

Not sure how this is happening but in extremely rare circumstances it is possible to hang indefinitely. See #57 workflow run for more info.

My guess is there is one very small outlying situation that causes a rwlock to be locked and used by a child node, but this shouldn't be possible with the well-tested circulation prevention algorithm. This definitely requires further debugging, but it is so rare and obscure that it is difficult to catch it and the details about what happened.

[Bowarc]

After a couple of generation, it just stops and i don't know why.

It appears to be 100% of the time with my current test, I pushed it at https://github.com/Bowarc/doodlai_jump/tree/ea955a6b681fcbaa2a4e3ec6d81f14970d5414b7

(The /ring package is responsible for training (the one hanging after a couple of generations), game is a lib for a rly simple version of doodle jump and display is to see the ai play)

[HyperCodec]

After a couple of generation, it just stops and i don't know why.

It appears to be 100% of the time with my current test, I pushed it at https://github.com/Bowarc/doodlai_jump/tree/ea955a6b681fcbaa2a4e3ec6d81f14970d5414b7

(The /ring package is responsible for training (the one hanging after a couple of generations), game is a lib for a rly simple version of doodle jump and display is to see the ai play)

Hmm so it's probably something with a recursive RwLock. I'll have to look into it further. It's probably some internal function causing a cyclic neuron dependency (like DFS not working or something).

[HyperCodec]

Btw @Bowarc can you use the serde feature to dump a json (or ron) file on the generation that hangs? (Probably the easiest way to do this would be to overwrite the same file with each generation and then stop the program when it hangs)

[Bowarc]

Ok, i'll do that tomorrow

[Bowarc]

Well, i stayed up longer than expected 😅
Here is the dna of every genome of a sim that froze

DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [RwLock { data: NeuronTopology { inputs: [(Input(3), -0.5625169)], bias: 0.59482414, activation: sigmoid
 }, poisoned: false, .. }], output_layer: [RwLock { data: NeuronTopology { inputs: [(Hidden(0), 0.9505495)], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.97373414), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }
DNA { network: NeuralNetworkTopology { input_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.19538373, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9611819, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5509694, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.31042653, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9654784, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.81183213, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.86611843, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.9298546, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8283311, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.8759112, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.4996699, activation: linear_activation
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [], bias: 0.5423544, activation: linear_activation
 }, poisoned: false, .. }], hidden_layers: [], output_layer: [RwLock { data: NeuronTopology { inputs: [], bias: 0.010660529, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(6), -0.1170296)], bias: 0.39411813, activation: sigmoid
 }, poisoned: false, .. }, RwLock { data: NeuronTopology { inputs: [(Input(7), -0.8857193), (Input(11), -0.97913766), (Input(2), 0.2923255), (Input(1), 0.26824117), (Input(2), -0.8934064), (Input(10), -0.19709682), (Input(9), -0.92098737), (Input(6), -0.9772694), (Input(7), 0.08727813), (Input(3), -0.61651254), (Input(9), 0.42674088), (Input(7), -0.801528), (Input(1), 0.6078919)], bias: 0.7993354, activation: sigmoid
 }, poisoned: false, .. }], mutation_rate: 0.01, mutation_passes: 3 } }

made a new commit if you wanna check it 8d75367

[HyperCodec]

Well, i stayed up longer than expected 😅 Here is the dna of every genome of a sim that froze...

Something I noticed here is that there are a lot inputs for Input layer neurons on one of the output neurons for each genome. I doubt this is just a result of evolution or something because of that huge ratio between it and the other neurons. Probably another issue to fix.

Anyways, I created #61 for the duplicate neuron references that are in the inputs to that output neuron.

[HyperCodec]

Merged #62, which is the main suspect of this issue.

@Bowarc Can you try to run with neat = { git = "https://github.com/hypercodec/neat", branch = "dev", features = ["whateveryouhadbefore"] } and see if it still hangs?

[Bowarc]

I've now tested over 3k generations, it seems to be stable, thank you for the fix (i had ["crossover", "rayon", "serde"] as features)

[HyperCodec]

Np

[HyperCodec]

You can use dev branch for now but it's not a good branch to stay on bc of large api changes, def change back to stable after next release.

[Bowarc]

Alright, thanks !