mirror of
https://github.com/jcreek/CosmicClash.git
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Josh Creek
74 lines
2.2 KiB
GDScript
74 lines
2.2 KiB
GDScript
class_name PolicyNetwork
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extends RefCounted
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# Minimal MLP forward pass for running trained policies in pure GDScript —
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# no .NET build or ONNX runtime needed. Weights come from a JSON file written
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# by training/export_policy.py (see TRAINING.md). The policy net is tiny
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# (31 → 64 → 64 → 7 by default), and the bot only thinks every few physics
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# ticks, so GDScript is plenty fast.
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#
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# JSON shape:
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# {
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# "input_size": 31,
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# "layers": [
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# {"weights": [[out x in floats]], "biases": [out floats], "activation": "tanh" | "linear"},
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# ...
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# ]
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# }
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var input_size: int = 0
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var _layers: Array = []
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static func load_from_file(path: String) -> PolicyNetwork:
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if not FileAccess.file_exists(path):
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push_error("PolicyNetwork: model file not found: %s" % path)
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return null
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var text := FileAccess.get_file_as_string(path)
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var data: Variant = JSON.parse_string(text)
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if data == null or not (data is Dictionary) or not data.has("layers"):
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push_error("PolicyNetwork: invalid model file: %s" % path)
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return null
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var net := PolicyNetwork.new()
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net.input_size = int(data.get("input_size", 0))
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for layer in data["layers"]:
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# Flatten each layer's weights into a PackedFloat64Array for speed
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var out_size: int = layer["biases"].size()
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var in_size: int = layer["weights"][0].size()
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var flat := PackedFloat64Array()
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flat.resize(out_size * in_size)
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var i := 0
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for row in layer["weights"]:
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for value in row:
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flat[i] = value
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i += 1
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var biases := PackedFloat64Array(layer["biases"])
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net._layers.append({
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"weights": flat,
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"biases": biases,
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"in_size": in_size,
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"out_size": out_size,
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"tanh": layer.get("activation", "linear") == "tanh",
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})
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return net
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func forward(observation: Array) -> Array:
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var x := PackedFloat64Array(observation)
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for layer in _layers:
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var in_size: int = layer["in_size"]
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var out_size: int = layer["out_size"]
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var weights: PackedFloat64Array = layer["weights"]
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var biases: PackedFloat64Array = layer["biases"]
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var y := PackedFloat64Array()
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y.resize(out_size)
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for row in out_size:
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var sum := biases[row]
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var offset := row * in_size
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for col in in_size:
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sum += weights[offset + col] * x[col]
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y[row] = tanh(sum) if layer["tanh"] else sum
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x = y
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return Array(x)
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