basic.py

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


# Define the neural network for state prediction
class StatePredictor(keras.Model):
    def __init__(self):
        super(StatePredictor, self).__init__()
        self.dense1 = layers.Dense(64, activation='relu')
        self.dense2 = layers.Dense(64, activation='relu')
        self.output_layer = layers.Dense(6)  # Predicts x_speed, y_speed, x_accel, y_accel, x_pos, y_pos

    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.dense2(x)
        return self.output_layer(x)


# Function to compute the loss
def compute_loss(model, states, next_states, lambda_reg):
    predictions = model(states)
    mse_loss = tf.reduce_mean(tf.square(next_states - predictions))

    # Placeholder for constraint evaluation; this should be replaced with your actual constraint logic
    constraints = np.random.rand(len(states))  # Example placeholder
    constraint_loss = tf.reduce_mean(tf.maximum(0.0, constraints - 0.1))  # Assume 0.1 is the threshold

    return mse_loss + lambda_reg * constraint_loss


# Exponential Family Likelihood Constraint Estimation
def likelihood_estimation(theta, state_t, state_t1):
    # Feature mapping
    phi = np.concatenate([state_t, state_t1])  # Simple concatenation as an example
    z = np.dot(theta, phi)
    return np.exp(z) / np.sum(np.exp(z))  # Normalize


# Q-learning setup
class QLearningAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.q_table = np.zeros((state_size, action_size))
        self.learning_rate = 0.1
        self.discount_factor = 0.95

    def update(self, state, action, reward, next_state):
        best_next_action = np.argmax(self.q_table[next_state])
        td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]
        td_error = td_target - self.q_table[state][action]
        self.q_table[state][action] += self.learning_rate * td_error


# Hyperparameters
num_episodes = 1000
lambda_reg = 0.1

# Initialize models
predictor = StatePredictor()
agent = QLearningAgent(state_size=100, action_size=4)  # Example sizes

# Training loop
for episode in range(num_episodes):
    # Reset environment and get initial state
    current_state = np.random.rand(6)  # Example state initialization
    done = False

    while not done:
        # Predict next state using the model
        next_state = predictor(np.array(current_state).reshape(1, -1)).numpy().flatten()

        # Assume some reward function
        reward = 1 if np.random.rand() > 0.5 else 0  # Placeholder reward logic

        # Update Q-learning agent
        action = np.random.choice(agent.action_size)  # Placeholder for action selection
        agent.update(current_state[0], action, reward, next_state[0])

        # Compute loss and update neural network
        loss = compute_loss(predictor, np.array(current_state).reshape(1, -1), next_state.reshape(1, -1), lambda_reg)
        predictor.optimizer.minimize(lambda: loss, var_list=predictor.trainable_variables)

        # Move to the next state
        current_state = next_state

        # Example termination condition
        if np.linalg.norm(current_state) < 0.1:  # Example condition
            done = True

print("Training completed.")