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vandomej 2025-09-07 22:08:20 -07:00
parent 5ab3c2382e
commit 95fdad1034
4 changed files with 103 additions and 60 deletions
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63
evolved-npcs/src/fighter_nn/mod.rs
View file

@ -3,7 +3,7 @@ extern crate fann;
pub mod fighter_context;
pub mod neural_network_utility;
use anyhow::{anyhow, Context};
use anyhow::{Context, anyhow};
use async_trait::async_trait;
use fann::{ActivationFunc, Fann};
use futures::future::join_all;
@ -114,8 +114,10 @@ impl GeneticNode for FighterNN {
})?;
let mut nn_shapes = HashMap::new();
let weight_initialization_amplitude = rng().random_range(0.0..NEURAL_NETWORK_INITIAL_WEIGHT_MAX);
let weight_initialization_range = -weight_initialization_amplitude..weight_initialization_amplitude;
let weight_initialization_amplitude =
rng().random_range(0.0..NEURAL_NETWORK_INITIAL_WEIGHT_MAX);
let weight_initialization_range =
-weight_initialization_amplitude..weight_initialization_amplitude;
// Create the first generation in this folder
for i in 0..POPULATION {
@ -201,7 +203,6 @@ impl GeneticNode for FighterNN {
i
};
let secondary_id = loop {
if allotted_simulations.is_empty() || allotted_simulations.len() == 1 {
// Select a random id
@ -243,11 +244,12 @@ impl GeneticNode for FighterNN {
let task = {
let self_clone = self.clone();
let semaphore_clone = context.gemla_context.shared_semaphore.clone();
let display_simulation_semaphore = context.gemla_context.visible_simulations.clone();
let display_simulation_semaphore =
context.gemla_context.visible_simulations.clone();
let folder = self_clone.folder.clone();
let generation = self_clone.r#generation;
let primary_nn = self_clone
.folder
.join(format!("{}", self_clone.r#generation))
@ -257,32 +259,36 @@ impl GeneticNode for FighterNN {
.join(format!("{}", generation))
.join(self_clone.get_individual_id(*secondary_id as u64))
.with_extension("net");
// Introducing a new scope for acquiring permits and running simulations
let simulation_result = async move {
let permit = semaphore_clone.acquire_owned().await
let permit = semaphore_clone
.acquire_owned()
.await
.with_context(|| "Failed to acquire semaphore permit")?;
let display_simulation = display_simulation_semaphore.try_acquire_owned().ok();
let (primary_score, secondary_score) = if let Some(display_simulation) = display_simulation {
let (primary_score, secondary_score) = if let Some(display_simulation) =
display_simulation
{
let result = run_1v1_simulation(&primary_nn, &secondary_nn, true).await?;
drop(display_simulation); // Explicitly dropping resources no longer needed
result
} else {
run_1v1_simulation(&primary_nn, &secondary_nn, false).await?
};
drop(permit); // Explicitly dropping resources no longer needed
debug!(
"{} vs {} -> {} vs {}",
primary_id, secondary_id, primary_score, secondary_score
);
Ok((*primary_id, primary_score, *secondary_id, secondary_score))
}; // Await the scoped async block immediately
// The result of the simulation, whether Ok or Err, is returned here.
// This ensures tx is dropped when the block exits, regardless of success or failure.
simulation_result
@ -300,7 +306,8 @@ impl GeneticNode for FighterNN {
// resolve results for any errors
let mut scores = HashMap::new();
for result in results.into_iter() {
let (primary_id, primary_score, secondary_id, secondary_score) = result.with_context(|| "Failed to run simulation")?;
let (primary_id, primary_score, secondary_id, secondary_score) =
result.with_context(|| "Failed to run simulation")?;
// If score exists, add the new score to the existing score
if let Some((existing_score, count)) = scores.get_mut(&(primary_id as u64)) {
@ -480,8 +487,9 @@ impl GeneticNode for FighterNN {
.with_context(|| format!("Failed to create directory {:?}", folder.join("0")))?;
let get_highest_scores = |fighter: &FighterNN| -> Vec<(u64, f32)> {
let mut sorted_scores: Vec<_> =
fighter.scores[fighter.r#generation as usize].iter().collect();
let mut sorted_scores: Vec<_> = fighter.scores[fighter.r#generation as usize]
.iter()
.collect();
sorted_scores.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
sorted_scores
.iter()
@ -537,7 +545,10 @@ impl GeneticNode for FighterNN {
run_1v1_simulation(&left_nn_path, &right_nn_path, false).await?
};
debug!("{} vs {} -> {} vs {}", left_nn_id, right_nn_id, left_score, right_score);
debug!(
"{} vs {} -> {} vs {}",
left_nn_id, right_nn_id, left_score, right_score
);
drop(permit);
@ -734,7 +745,11 @@ fn should_continue(scores: &[HashMap<u64, f32>], lenience: u64) -> Result<bool,
debug!(
"Highest Q3 value: {} at generation {}, Highest Median value: {} at generation {}, Continuing? {}",
highest_q3_value, generation_with_highest_q3 + 1, highest_median, generation_with_highest_median + 1, result
highest_q3_value,
generation_with_highest_q3 + 1,
highest_median,
generation_with_highest_median + 1,
result
);
Ok(result)
@ -826,10 +841,7 @@ async fn run_1v1_simulation(
trace!(
"Executing the following command {} {} {} {}",
GAME_EXECUTABLE_PATH,
config1_arg,
config2_arg,
disable_unreal_rendering_arg
GAME_EXECUTABLE_PATH, config1_arg, config2_arg, disable_unreal_rendering_arg
);
trace!("Running simulation for {} vs {}", nn_1_id, nn_2_id);
@ -906,8 +918,7 @@ async fn read_score_from_file(file_path: &Path, nn_id: &str) -> Result<f32, io::
"NN ID not found in scores file",
));
}
Err(_) =>
{
Err(_) => {
if attempts >= 2 {
// Attempt 5 times before giving up.
return Ok(-100.0);

84
evolved-npcs/src/fighter_nn/neural_network_utility.rs
View file

@ -1,12 +1,13 @@
use std::{cmp::min, cmp::Ordering, collections::HashMap, ops::Range};
use std::{cmp::Ordering, cmp::min, collections::HashMap, ops::Range};
use anyhow::Context;
use fann::{ActivationFunc, Fann};
use gemla::error::Error;
use rand::{
Rng,
distr::{Distribution, Uniform},
rng,
seq::IteratorRandom,
rng, Rng,
};
use super::{
@ -208,13 +209,37 @@ pub fn consolidate_old_connections(
to_non_bias_network_id(connection.from_neuron, &primary_shape);
let original_to_neuron =
to_non_bias_network_id(connection.to_neuron, &primary_shape);
trace!("Primary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]", previous_new_id, new_id, original_from_neuron, original_to_neuron, connection.weight, found_in_primary, connection.from_neuron, connection.to_neuron, previous_neuron_id, neuron_id);
trace!(
"Primary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]",
previous_new_id,
new_id,
original_from_neuron,
original_to_neuron,
connection.weight,
found_in_primary,
connection.from_neuron,
connection.to_neuron,
previous_neuron_id,
neuron_id
);
} else {
let original_from_neuron =
to_non_bias_network_id(connection.from_neuron, &secondary_shape);
let original_to_neuron =
to_non_bias_network_id(connection.to_neuron, &secondary_shape);
trace!("Secondary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]", previous_new_id, new_id, original_from_neuron, original_to_neuron, connection.weight, found_in_primary, connection.from_neuron, connection.to_neuron, previous_neuron_id, neuron_id);
trace!(
"Secondary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]",
previous_new_id,
new_id,
original_from_neuron,
original_to_neuron,
connection.weight,
found_in_primary,
connection.from_neuron,
connection.to_neuron,
previous_neuron_id,
neuron_id
);
}
let translated_from = to_bias_network_id(previous_new_id, &new_shape);
let translated_to = to_bias_network_id(new_id, &new_shape);
@ -222,10 +247,7 @@ pub fn consolidate_old_connections(
} else {
trace!(
"Connection not found for ({}, {}) -> ({}, {})",
previous_new_id,
new_id,
previous_neuron_id,
neuron_id
previous_new_id, new_id, previous_neuron_id, neuron_id
);
}
}
@ -317,23 +339,43 @@ pub fn consolidate_old_connections(
to_non_bias_network_id(connection.from_neuron, &primary_shape);
let original_to_neuron =
to_non_bias_network_id(connection.to_neuron, &primary_shape);
trace!("Primary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]", bias_neuron, translated_neuron_id, original_from_neuron, original_to_neuron, connection.weight, found_in_primary, connection.from_neuron, connection.to_neuron, bias_neuron, neuron_id);
trace!(
"Primary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]",
bias_neuron,
translated_neuron_id,
original_from_neuron,
original_to_neuron,
connection.weight,
found_in_primary,
connection.from_neuron,
connection.to_neuron,
bias_neuron,
neuron_id
);
} else {
let original_from_neuron =
to_non_bias_network_id(connection.from_neuron, &secondary_shape);
let original_to_neuron =
to_non_bias_network_id(connection.to_neuron, &secondary_shape);
trace!("Secondary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]", bias_neuron, translated_neuron_id, original_from_neuron, original_to_neuron, connection.weight, found_in_primary, connection.from_neuron, connection.to_neuron, bias_neuron, neuron_id);
trace!(
"Secondary: Adding connection from ({} -> {}) translated to ({:?} -> {:?}) with weight {} for primary:{} [{} -> {}] [{} -> {}]",
bias_neuron,
translated_neuron_id,
original_from_neuron,
original_to_neuron,
connection.weight,
found_in_primary,
connection.from_neuron,
connection.to_neuron,
bias_neuron,
neuron_id
);
}
new_fann.set_weight(bias_neuron, translated_neuron_id, connection.weight);
} else {
trace!(
"Connection not found for bias ({}, {}) -> ({}, {}) primary: {}",
bias_neuron,
neuron_id,
bias_neuron,
translated_neuron_id,
is_primary
bias_neuron, neuron_id, bias_neuron, translated_neuron_id, is_primary
);
}
}
@ -388,8 +430,7 @@ pub fn crossbreed_neuron_arrays(
if neuron_id >= &segment.0 && neuron_id <= &segment.1 {
// We need to do something different depending on whether the neuron layer is, lower, higher or equal to the target layer
match layer.cmp(&current_layer)
{
match layer.cmp(&current_layer) {
Ordering::Equal => {
new_neurons.push((*neuron_id, is_primary, current_layer, 0));
@ -399,7 +440,7 @@ pub fn crossbreed_neuron_arrays(
secondary_last_layer = current_layer;
}
}
Ordering:: Less => {
Ordering::Less => {
// If it's in an earlier layer, add it to the earlier layer
// Check if there's a lower id from the same individual in that earlier layer
// As long as there isn't a neuron from the other individual in between the lower id and current id, add the id values from the same individual
@ -455,7 +496,12 @@ pub fn crossbreed_neuron_arrays(
.filter(|(id, l)| id > &highest_id.0 && *l == layer - 1)
.collect::<Vec<_>>();
for (neuron_id, _) in neurons_to_add {
new_neurons.push((*neuron_id, is_primary, current_layer, 0));
new_neurons.push((
*neuron_id,
is_primary,
current_layer,
0,
));
if is_primary {
primary_last_layer = current_layer;

2
evolved-npcs/src/main.rs
View file

@ -68,4 +68,4 @@ fn main() -> Result<()> {
info!("Finished in {:?}", now.elapsed());
Ok(())
}
}

14
visualize_networks.py
View file

@ -100,19 +100,5 @@ def visualize_fann_network(network_file):
plt.show()
# Path to the FANN network file
fann_path = 'F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_4f2be613-ab26-4384-9a65-450e043984ea\\6\\4f2be613-ab26-4384-9a65-450e043984ea_fighter_nn_0.net'
# fann_path = "F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_fc294503-7b2a-40f8-be59-ccc486eb3f79\\0\\fc294503-7b2a-40f8-be59-ccc486eb3f79_fighter_nn_0.net"
# fann_path = 'F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_99c30a7f-40ab-4faf-b16a-b44703fdb6cd\\0\\99c30a7f-40ab-4faf-b16a-b44703fdb6cd_fighter_nn_0.net'
# Has a 4 layer network
# # Generation 1
# fann_path = "F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98\\1\\16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98_fighter_nn_0.net"
# # Generation 5
# fann_path = "F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98\\5\\16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98_fighter_nn_0.net"
# # Generation 10
# fann_path = "F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98\\10\\16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98_fighter_nn_0.net"
# # Generation 20
# fann_path = "F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98\\20\\16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98_fighter_nn_0.net"
# # Generation 32
# fann_path = "F:\\\\vandomej\\Projects\\dootcamp-AI-Simulation\\Simulations\\fighter_nn_16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98\\32\\16dfa1b4-03c7-45a6-84b4-22fe3c8e2d98_fighter_nn_0.net"
fann_path = select_file()
visualize_fann_network(fann_path)