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DQNAgent/DQNAgent.py

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+import QLAgent
+from perceptionmodule import image_recognition, text_processing 
+from learningmodule import supervised_learning, QLearningAgent, run_q_learning, reinforcement_learning
+from rlmodule import execute_action_and_get_reward
+from reasoningmodule import rule_based_reasoning, decision_making
+from lpmodule import simple_chatbot
+from integrationmodule import integrate_modules
+
+
+
+def image_recognition(image_data):
+    pass
+
+def text_processing(text_data):
+    pass
+
+# Example data
+image_data = "path_to_image.jpg"
+text_data = "This is a sample text."
+user_input = "How are you?"
+
+# Perception Module
+image_result = image_recognition(image_data)
+text_result = text_processing(text_data)
+
+# Learning Module
+supervised_result = supervised_learning(X_train, y_train)
+reinforcement_result = reinforcement_learning()
+
+# Reasoning Module
+rule_based_result = rule_based_reasoning(text_data)
+decision_making_result = decision_making(X_train, y_train)
+
+# Language Processing Module
+chatbot_response = simple_chatbot(user_input)
+
+# Integration Module
+final_output = integrate_modules(image_result, text_result, supervised_result,
+                                reinforcement_result, rule_based_result,
+                                decision_making_result, chatbot_response)
+def cagi_agent(states):
+    # Placeholder function, replace with actual state representation logic
+    return states[0]
+
+# RL Agent
+rl_agent = QLearningAgent(num_actions=3)  # Assuming 3 possible actions
+
+def execute_action_and_get_reward(action):
+    # Placeholder function, replace with actual action execution and reward logic
+    return 1.0  # Placeholder reward
+
+def integrate_modules(image_data, text_data, user_input):
+    perception_output = image_recognition(image_data)
+    learning_output = supervised_learning(text_data)
+    reasoning_output = rule_based_reasoning(user_input)
+    language_output = simple_chatbot(user_input)
+
+    # RL Module
+    current_state = cagi_agent(environment_states)
+    rl_action = rl_agent.select_action(current_state)
+    rl_reward = execute_action_and_get_reward(rl_action)
+    next_state = cagi_agent(environment_states)
+    rl_agent.update_q_table(current_state, rl_action, rl_reward, next_state)
+
+    final_output["rl_learning"] = {"action": rl_action, "reward": rl_reward}
+
+    return final_output
+
+    # Load a sample dataset for illustration (replace with your dataset)
+iris = load_iris()
+X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
+
+# Other imports and definitions from your script
+
+# Example usage
+image_data = "path_to_image.jpg"
+text_data = "This is a sample text."
+user_input = "How are you?"
+
+environment_states = ["State1", "State2", "State3"]
+
+output = integrate_modules(image_data, text_data, user_input)
+print("CAGI Agent Output:", output)
+
+env = gym.make('FrozenLake-v1')
+
+# Ensure that observation_space and action_space are valid gym.spaces objects
+observation_space = env.observation_space
+action_space = env.action_space
+
+# Initialize the QLearningAgent with q_table, observation_space, and action_space
+q_table = ...  # Define or load your q_table
+agent = QLearningAgent(q_table, observation_space, action_space)
+
+num_episodes = 100
+
+# Get the number of episodes
+num_episodes = get_num_episodes()
+
+# Call run_q_learning using the created agent
+run_q_learning(agent, env, num_episodes)