| import torch | |
| from agents.perception_module import PerceptionAgent | |
| from agents.decision_module import DecisionAgent | |
| from agents.action_module import ActionAgent | |
| class MasterAgent: | |
| def __init__(self, config): | |
| self.perception_agent = PerceptionAgent(config) | |
| self.decision_agent = DecisionAgent(config) | |
| self.action_agent = ActionAgent(config) | |
| self.reinforcement_learning = config.get("reinforcement_learning", True) | |
| def forward(self, inputs): | |
| # Process inputs through the perception agent | |
| perception_output = self.perception_agent(inputs) | |
| # Pass perception results to decision agent | |
| decision_output = self.decision_agent(perception_output) | |
| # Execute the chosen action | |
| action_output = self.action_agent(decision_output) | |
| return action_output | |
| def learn(self, feedback): | |
| # Implement reinforcement learning logic to adjust task allocation | |
| if self.reinforcement_learning: | |
| # Update sub-agent weights based on feedback | |
| pass | |