# Core Q-learning Class
import numpy as np

print("About to instantiate QLearningAgent")
agent = QLearningAgent(state_size, action_size, learning_rate, discount_factor, epsilon)

class QLearningAgent:
    def __init__(self, state_size, action_size, learning_rate, discount_factor, epsilon):
        # Existing attributes
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.epsilon = epsilon
        self.q_table = np.zeros((state_size, action_size))

        # Initialize neurotransmitter systems
        self.dopamine_system = DopamineSystem(learning_rate=0.01, prediction_error_sensitivity=0.1)
        self.norepinephrine_system = NorepinephrineSystem(base_firing_rate=1.0)
        self.acetylcholine_system = AcetylcholineSystem(attention_weights=np.ones(action_size))
        self.serotonin_system = SerotoninSystem(mood_state_vector_space=VectorSpace(3), motivation_vector_space=VectorSpace(3))
        self.glutamate_system = GlutamateSystem(connectivity_matrix=np.zeros((state_size, action_size)))
        self.gaba_system = GABA_System(inhibition_matrix=np.zeros((state_size, action_size)))

        # Initialize additional neuromodulator systems
        self.endorphins_system = EndorphinsSystem(familiar_environment_factor=1.2)  # Example factor
        self.substance_p_system = SubstancePSystem(error_factor=0.8)  # Example factor
        self.anandamide_system = AnandamideSystem(mood_stabilization_factor=1.1)  # Example factor
        
   
        
    def select_action(self, state):
        if np.random.rand() < self.epsilon:
            # Explore: choose a random action
            return np.random.randint(self.action_size)
        else:
            # Exploit: choose the best action based on modulated Q-values
            q_values_modulated = self.modulate_q_values(state)
            return np.argmax(q_values_modulated)
    
    def modulate_q_values(self, state):
        # Assume state is an integer index for simplicity
        q_values = self.q_table[state]
        
        # Apply modulators
        q_values = self.dopamine_system.modulate(q_values)
        q_values = self.norepinephrine_system.modulate(q_values)
        q_values = self.acetylcholine_system.modulate(q_values)
        q_values = self.serotonin_system.modulate(q_values)
        q_values = self.glutamate_system.modulate(q_values)
        q_values = self.gaba_system.modulate(q_values)
        
        return q_values
    
    def update(self, state, action, reward, next_state, is_novel):
        # Modulate reward using the Endorphins system
        modulated_reward = self.endorphins_system.modulate_reward(reward, is_novel)

        # Apply learning modulation using Substance P system
        learning_modulation = self.substance_p_system.modulate_learning(reward)

        # Q-learning update with modulated reward and learning rate
        old_value = self.q_table[state, action]
        next_max = np.max(self.q_table[next_state])
        new_value = old_value + self.learning_rate * learning_modulation * (modulated_reward + self.discount_factor * next_max - old_value)
        self.q_table[state, action] = new_value
        
        # Update neurotransmitter systems
        self.dopamine_system.update(state, action, reward)
        self.endorphins_system.update(state, action, modulated_reward)
        self.substance_P_system.update(state, action, reward)
        self.oxytocin_system.update(state, action)
        self.anandamide_system.update(state, action)
        self.norepinephrine_system.update(state, action)
        self.acetylcholine_system.update(state, action)
        self.serotonin_system.update(state, action)
        self.glutamate_system.update(state, action)
        self.gaba_system.update(state, action)
        # … other updates for neurotransmitter systems
 
class ChatEnvironment:
    def __init__(self, max_input_length, max_response_length):
        self.max_input_length = max_input_length
        self.max_response_length = max_response_length
        self.current_input = np.zeros(max_input_length, dtype=int)

    def reset(self):
        self.current_input = np.zeros(self.max_input_length, dtype=int)
        return self.get_state()

    def step(self, action):
        response = self.generate_response(action)
        next_state = self.get_state()
        reward = self.get_user_feedback(response)
        done = self.is_conversation_over(response)
        return next_state, reward, done, {}

    def get_state(self):
        # State representation based on current input
        return self.current_input

    def generate_response(self, action):
        # Convert action (ASCII values) to a response string
        response = ''.join([chr(a) for a in action])
        return response

    def process_input(self, user_input):
        # Convert user input to ASCII values and update the current input
        ascii_values = [ord(c) for c in user_input[:self.max_input_length]]
        self.current_input[:len(ascii_values)] = ascii_values

    def get_user_feedback(self, response):
        print(f"AI: {response}")
        user_feedback = input("Was this response expected? (yes/no): ")
        return 1 if user_feedback.lower() == 'yes' else -1

    def is_conversation_over(self, response):
        return response.strip() == "Goodbye!"

# Neurotransmitter System Classes:

class DopamineSystem:
    def __init__(self, learning_rate, prediction_error_sensitivity):
        self.learning_rate = learning_rate  # Learning rate of the dopamine system
        self.prediction_error_sensitivity = prediction_error_sensitivity
        self.prediction_error = 0  # Initial prediction error

    def calculate_prediction_error(self, expected_outcome, actual_outcome):
        # Calculate the prediction error
        self.prediction_error = actual_outcome - expected_outcome

    def update_learning_rate(self):
        # Adjust the learning rate based on the prediction error
        self.learning_rate += self.prediction_error_sensitivity * self.prediction_error

    def modulate(self, q_values):
        # Modulate Q-values based on the prediction error
        # Placeholder logic: adjust q_values based on prediction error
        modulated_q_values = q_values * (1 + self.learning_rate * self.prediction_error)
        return modulated_q_values

    def update(self, state, action, reward):
        # Update internal state based on the outcome
        # Example: Calculate prediction error and update learning rate
        expected_reward = self.q_table[state, action]
        self.calculate_prediction_error(expected_reward, reward)
        self.update_learning_rate()


#NorepinephrineSystem:


class NorepinephrineSystem:
    def init(self, base_firing_rate):
        self.firing_rate = base_firing_rate
        
    def adjust_firing_rate(self, attention_level):
        self.firing_rate *= attention_level



#AcetylcholineSystem:


class AcetylcholineSystem:
    def init(self, attention_weights):
        self.attention_weights = attention_weights
        
    def modulate_attention(self, expected_rewards):
        # Modulate attention based on the expected rewards
        pass  # Implementation code here 



#SerotoninSystem:


class SerotoninSystem:
    def init(self, mood_state_vector_space, motivation_vector_space):
        self.mood_state_vector_space = mood_state_vector_space
        self.motivation_vector_space = motivation_vector_space
        
    def update_mood(self, mood_vector):
        self.mood_state_vector_space.adjust(mood_vector)
        
    def update_motivation(self, motivation_vector):
        self.motivation_vector_space.adjust(motivation_vector)



#Glutamate_System:


class GlutamateSystem:
    def __init__(self, connectivity_matrix):
        self.connectivity_matrix = connectivity_matrix  # A matrix representing neural connections

    def update_connectivity(self, firing_pattern):
        # Update the connectivity matrix based on the neuron's firing pattern
        # Placeholder: implement logic to adjust the connectivity matrix
        pass

    def modulate(self, q_values, state):
        # Modulate Q-values based on the current connectivity state
        # Placeholder: implement modulation logic based on connectivity_matrix
        modulated_q_values = q_values  # Modify this according to your logic
        return modulated_q_values



#GABASystem:


class GABA_System:
    def init(self, inhibition_matrix):
        self.inhibition_matrix = inhibition_matrix
        
    def apply_inhibition(self, neuron_group):
        # Apply inhibitory effects to the given neuron group
        pass  # Implementation code here
        
        
        
#EndorphinesSystem:
    

class EndorphinsSystem:
    def __init__(self, familiar_environment_factor):
        self.familiar_environment_factor = familiar_environment_factor
        self.is_familiar_environment = False

    def update(self, state, action, reward):
        # Logic to determine if the current environment is familiar
        # Placeholder: set self.is_familiar_environment based on state

        def modulate_reward(self, reward, is_novel):
            if self.is_familiar_environment and is_novel:
             return reward * self.familiar_environment_factor
            return reward



#Subsance P system:


class SubstancePSystem:
    def __init__(self, error_factor):
        self.error_factor = error_factor

    def modulate_learning(self, reward):
        if reward < 0:  # Assuming negative reward indicates error
            return self.error_factor
        return 1.0



#Anadamide System


class AnandamideSystem:
    def __init__(self, mood_stabilization_factor):
        self.mood_stabilization_factor = mood_stabilization_factor

    def modulate_q_values(self, q_values):
        # Apply mood stabilization to Q-values
        stabilized_q_values = q_values * self.mood_stabilization_factor
        return stabilized_q_values


#2. Vector Spaces for Emotional States: Representations of different emotional states that can adjust over time or in response to stimuli.


class VectorSpace:
    def init(self, dimensions):
        self.vectors = np.zeros(dimensions)
    
    def adjust(self, vector_update):
        self.vectors += vector_update


# Instantiate ChatEnvironment
chat_env = ChatEnvironment(max_input_length=50, max_response_length=30)

#3. Q-learning Parameters: These are part of the Q-learning algorithm for decision-making.

class QLearningParameters:
    def init(self, alpha, gamma):
        self.alpha = alpha  # Learning rate
        self.gamma = gamma  # Discount factor



#4. Q-learning Algorithm: This core decision-making module would use various parameters and the neurotransmitter-like mechanisms to learn and make decisions.


class QLearningAgent:
    def init(self, q_table, parameters):
        self.q_table = q_table
        self.parameters = parameters

# Example parameters - adjust these based on your environment
state_size = 100  # The number of states in the environment
action_size = (30^2)*(20^2)   # The number of possible actions the agent can take
learning_rate = 0.01  # The rate at which the agent should learn
discount_factor = 0.95  # The discount factor for future rewards
epsilon = 0.01  # The probability of choosing a random action over the best action

# Instantiate the QLearningAgent with the specified parameters
agent = QLearningAgent(state_size, action_size, learning_rate, discount_factor, epsilon)


# Training loop
num_episodes = 1000  # Number of episodes for training
for episode in range(num_episodes):
    state = chat_env.reset()  # Reset the environment at the start of each episode
    done = False

    while not done:
        action = agent.select_action(state)  # Agent selects an action
        next_state, reward, done, _ = chat_env.step(action)  # Perform the action on the environment

        # Update the agent with the results of the action
        agent.update(state, action, reward, next_state, is_novel=False)  # 'is_novel' flag needs to be determined

        state = next_state  # Update state

can you design a q learning ml to fit this model? it can be simple and sent over multiple prompts