Artificial Intelligence, Data Science, Machine Learning, Reinforcement Learning, Programming.
for 1 episode while not done: steps+=1 # Get the action probabilities from the policy network action_probs = policy_network.predict(np.array([state]))[0] # Choose an action based on the action probabilities action = np.random.choice(num_actions, p=action_probs) next_state, reward, done, info =