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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 | from typing import Dict, Any, Optional
from src.tools.base import ToolConnector
from src.brain.runtime import BrainRuntime
class ActInEnvironmentConnector(ToolConnector):
def __init__(self):
super().__init__(
name="act_in_environment",
description="Executes a physical or simulated locomotion/foraging action in the virtual ecosystem.",
timeout_sec=5.0
)
self.agent_pos = [0.0, 0.0]
self.heading_rad = 0.0
@property
def input_schema(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"action_type": {"type": "string", "enum": ["move_forward", "turn_left", "turn_right", "forage"]},
"magnitude": {"type": "number", "default": 1.0}
},
"required": ["action_type"]
}
@property
def output_schema(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"new_position": {"type": "array"},
"heading": {"type": "number"},
"foraged_reward": {"type": "number"}
},
"required": ["new_position", "heading", "foraged_reward"]
}
def _execute(self, params: Dict[str, Any], execution_id: str) -> Dict[str, Any]:
act = params["action_type"]
mag = float(params.get("magnitude", 1.0))
reward = 0.0
if act == "move_forward":
self.agent_pos[0] += mag * 0.1
self.agent_pos[1] += mag * 0.1
elif act == "turn_left":
self.heading_rad += 0.1 * mag
elif act == "turn_right":
self.heading_rad -= 0.1 * mag
elif act == "forage":
reward = 0.5 * mag
return {
"new_position": [round(p, 3) for p in self.agent_pos],
"heading": round(self.heading_rad, 3),
"foraged_reward": reward
}
class InspectSelfConnector(ToolConnector):
def __init__(self, brain: BrainRuntime):
super().__init__(
name="inspect_self",
description="Inspects internal computational state, drives, attention, and spike telemetry of the brain.",
timeout_sec=5.0
)
self.brain = brain
@property
def input_schema(self) -> Dict[str, Any]:
return {"type": "object", "properties": {}}
@property
def output_schema(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"step": {"type": "integer"},
"total_spikes": {"type": "integer"},
"energy": {"type": "number"},
"curiosity": {"type": "number"},
"social": {"type": "number"},
"integrity": {"type": "number"},
"num_neurons": {"type": "integer"},
"num_synapses": {"type": "integer"}
},
"required": ["step", "energy", "num_neurons", "num_synapses"]
}
def _execute(self, params: Dict[str, Any], execution_id: str) -> Dict[str, Any]:
return {
"step": self.brain.state.step_count,
"total_spikes": self.brain.state.total_spikes,
"energy": round(self.brain.state.drives.energy, 4),
"curiosity": round(self.brain.state.drives.curiosity, 4),
"social": round(self.brain.state.drives.social, 4),
"integrity": round(self.brain.state.drives.integrity, 4),
"num_neurons": self.brain.graph.num_neurons,
"num_synapses": self.brain.graph.num_synapses
}
class SleepConnector(ToolConnector):
def __init__(self, brain: BrainRuntime):
super().__init__(
name="sleep",
description="Enters sleep/quiescent state to replenish homeostatic energy and repair neural integrity.",
timeout_sec=5.0
)
self.brain = brain
@property
def input_schema(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"duration_cycles": {"type": "integer", "default": 5}
}
}
@property
def output_schema(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"energy_restored": {"type": "number"},
"current_energy": {"type": "number"}
},
"required": ["energy_restored", "current_energy"]
}
def _execute(self, params: Dict[str, Any], execution_id: str) -> Dict[str, Any]:
cycles = int(params.get("duration_cycles", 5))
before_e = self.brain.state.drives.energy
restored = min(1.0 - before_e, 0.1 * cycles)
self.brain.state.drives.energy = min(1.0, before_e + restored)
self.brain.state.drives.integrity = min(1.0, self.brain.state.drives.integrity + 0.05 * cycles)
return {
"energy_restored": round(restored, 4),
"current_energy": round(self.brain.state.drives.energy, 4)
}
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