Artificial Intelligence, Data Science, Machine Learning, Reinforcement Learning, Programming.
Keep track of the states, actions, and rewards for each step in the episode states = [] actions = [] rewards = [] # Run the episode while True: # Get the action probabilities from the policy network action_probs = policy_network.predict(np.array([state]))[0] # Choose an action based on the action