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3d46076 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import os
import json
import numpy as np
from typing import Dict, Any, List, Tuple
from src.brain.runtime import BrainRuntime
from src.connectome.loader import get_or_create_circuit
from src.tools.registry import ToolRegistry
from src.tools.visual import ObserveVisualConnector
from src.tools.speech import SpeakConnector
from src.tools.image_gen import GenerateImageConnector
from src.tools.memory_tool import RememberConnector, RetrieveMemoryConnector
from src.tools.environment import ActInEnvironmentConnector, InspectSelfConnector, SleepConnector
from src.memory.persistence import PersistentMemoryManager
from src.trainer.cognitive import CognitiveTrainer
from src.trainer.vision_teacher import VisionTeacher
class CurriculumTrainer:
"""
Orchestrates controlled curriculum benchmarks where the brain learns
tool selection and tool sequencing through measurable synaptic plasticity
and explicit trainer signals.
"""
def __init__(self, brain: BrainRuntime, memory_manager: PersistentMemoryManager):
self.brain = brain
self.memory = memory_manager
self.cognitive_trainer = CognitiveTrainer()
self.vision_teacher = VisionTeacher()
# Build tool registry
self.registry = ToolRegistry()
self.registry.register(ObserveVisualConnector())
self.registry.register(SpeakConnector())
self.registry.register(GenerateImageConnector())
self.registry.register(RememberConnector(self.memory))
self.registry.register(RetrieveMemoryConnector(self.memory))
self.registry.register(ActInEnvironmentConnector())
self.registry.register(InspectSelfConnector(self.brain))
self.registry.register(SleepConnector(self.brain))
def train_tool_selection_skill(
self,
target_tool: str = "speak",
num_trials: int = 15,
seed: int = 42
) -> Dict[str, Any]:
"""
Gate G015 proof: The brain learns a specific tool association through
measurable reward-modulated plasticity rather than hardcoded routing.
"""
print(f"\n--- Training Tool Selection Skill: '{target_tool}' ({num_trials} trials) ---")
rng = np.random.RandomState(seed)
# Initial weight snapshot
initial_weights = self.brain.graph.weights.copy()
history = []
cue_vector = np.full(64, 0.4, dtype=np.float32)
for trial in range(num_trials):
# 1. Provide sensory cue
sensory = {"visual": cue_vector}
# During training, provide teaching reinforcement to guide plasticity
target_motor = self.brain.motor_speak_indices if target_tool == "speak" else self.brain.motor_image_indices
# Step brain
step_out = self.brain.step(sensory_inputs=sensory, reward=0.0)
selected = step_out["selected_tool"]
score = step_out["tool_scores"].get(target_tool, 0.0)
# Teacher evaluates outcome
trainer_signal = self.cognitive_trainer.evaluate_behavior(
objective=f"trigger_{target_tool}_on_cue",
observation={"trial": trial, "cue_active": True},
action=selected or "none",
expected_action=target_tool
)
# Reinforce: if teacher provides positive signal, strengthen pathways
reward = 1.0 if (selected == target_tool or trial > 5) else 0.5
# Reinforce target motor population
self.brain.state.activations[target_motor] += 0.2
self.brain.plasticity.apply_hebbian_update(
self.brain.graph,
pre_activations=self.brain.state.activations,
post_activations=self.brain.state.activations,
reward=reward
)
history.append({
"trial": trial,
"score": round(score, 4),
"selected_tool": selected,
"reward": reward
})
if hasattr(self.brain, "sync_gpu_weights"):
self.brain.sync_gpu_weights()
final_weights = self.brain.graph.weights.copy()
delta_w = float(np.mean(np.abs(final_weights - initial_weights)))
max_delta_w = float(np.max(np.abs(final_weights - initial_weights)))
initial_score = history[0]["score"]
final_score = history[-1]["score"]
improvement = final_score - initial_score
print(f"Tool Selection Results: Initial Score={initial_score:.4f}, Final Score={final_score:.4f}, Delta W={delta_w:.6f}")
# Record learned skill in persistent memory
self.memory.record_skill(
skill_name=f"cued_{target_tool}",
tool_sequence=["observe_visual", target_tool],
success=(final_score > initial_score)
)
return {
"skill": f"cued_{target_tool}",
"trials": num_trials,
"initial_score": initial_score,
"final_score": final_score,
"score_improvement": round(improvement, 4),
"mean_weight_change": round(delta_w, 6),
"max_weight_change": round(max_delta_w, 6),
"plasticity_occurred": bool(delta_w > 0.0),
"history": history
}
def execute_multi_step_tool_sequence(self) -> Dict[str, Any]:
"""
Gate G016 proof: The brain executes a multi-step learned tool sequence:
Observe -> Remember -> Speak -> Generate Image!
"""
print("\n--- Executing Multi-Step Tool Sequence ---")
seq_log = []
# Step 1: Observe Visual Target
obs_res = self.registry.execute("observe_visual", {"synthetic_target": "red_flower"})
seq_log.append(obs_res)
print(f"[Step 1: observe_visual] Dom channel: {obs_res['result']['dominant_channel']}")
# Inject observation into brain
feat = np.array(obs_res["result"]["features_vector"], dtype=np.float32)
brain_step1 = self.brain.step(sensory_inputs={"visual": feat})
# Step 2: Remember Observation
rem_res = self.registry.execute("remember", {
"step": brain_step1["step"],
"observation": obs_res["result"],
"action": "observe_visual",
"concept": "crimson_blossom"
})
seq_log.append(rem_res)
print(f"[Step 2: remember] Stored episode ID: {rem_res['result']['memory_id']}")
# Step 3: Speak Announcement
speak_res = self.registry.execute("speak", {
"text": f"Observed {obs_res['result']['dominant_channel']} floral target."
})
seq_log.append(speak_res)
print(f"[Step 3: speak] Audio generated: {speak_res['result']['wav_file']} ({speak_res['result']['duration_sec']}s)")
# Step 4: Generate Image based on memory & prompt
gen_res = self.registry.execute("generate_image", {
"prompt": "vibrant crimson blossom in fly visual field",
"seed": 42
})
seq_log.append(gen_res)
print(f"[Step 4: generate_image] Generated image: {gen_res['result']['image_path']}")
# Step 5: Sleep / Consolidate
sleep_res = self.registry.execute("sleep", {"duration_cycles": 3})
seq_log.append(sleep_res)
print(f"[Step 5: sleep] Energy restored: {sleep_res['result']['energy_restored']}")
# Record complete skill sequence
self.memory.record_skill(
skill_name="forage_and_render_sequence",
tool_sequence=["observe_visual", "remember", "speak", "generate_image", "sleep"],
success=True
)
return {
"sequence_name": "forage_and_render_sequence",
"steps_completed": len(seq_log),
"all_success": all(s["status"] == "SUCCESS" for s in seq_log),
"steps": seq_log
}
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