Reinforcement Learning, Artificial Intelligence, Machine Learning, Data Science, Statistics.
action).tolist() x, y = next_state if x < 0 or x >= WORLD_SIZE or y < 0 or y >= WORLD_SIZE: reward = -1.0 next_state = state else: reward = 0 return next_state, reward with Linear system A = -1 * np.eye(WORLD_SIZE * WORLD_SIZE) b = np.zeros(WORLD_SIZE * WORLD_SIZE) for i in range(WORLD_SIZE): for j