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File size: 2,299 Bytes
5b6725d | 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 | import engine
import json
import random
class MycoController:
def __init__(self):
self.position = [1, 1] # 3x3 Grid (0-2)
self.history = []
def get_agent_decision(self, current_mushroom, collection):
"""
Queries Gemma for a decision.
Note: We use engine._llm but provide a specific 'Agent' prompt.
"""
prompt = f"""
Current Position: {self.position}
Mushroom in clearing: {'Yes' if current_mushroom else 'No'}
Collection count: {len(collection)}
Decide your next action: 'move', 'search', 'study', 'collect', or 'wait'.
If 'move', provide target coordinate (e.g., [1, 2]).
Respond ONLY in JSON format:
{{"action": "...", "target": [x, y], "thought": "..."}}
"""
# We reuse the existing engine._llm functionality
response = engine._llm(prompt)
# Simple parser
try:
# Extract JSON from potential markdown text
start = response.find("{")
end = response.rfind("}") + 1
return json.loads(response[start:end])
except:
return {"action": "wait", "target": None, "thought": "Thinking..."}
def run_tick(self, current_mushroom, collection):
"""
This is the heartbeat of the AI.
It decides and then executes via engine functions.
"""
decision = self.get_agent_decision(current_mushroom, collection)
action = decision.get("action")
result = {"action_taken": action, "thought": decision.get("thought")}
# EXECUTION LAYER (calling existing engine functions)
if action == "move":
self.position = decision.get("target", self.position)
elif action == "search":
# We call the engine function directly
mushroom, current, history = engine.discover_mushroom(collection)
result["data"] = {"mushroom": mushroom, "current": current}
elif action == "collect":
if current_mushroom:
coll, hist = engine.collect_current(current_mushroom, collection, self.history)
result["data"] = {"collection": coll}
return result
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