Artificial Intelligence, Data Science, Machine Learning, Reinforcement Learning, Programming.
probabilities action = np.random.choice(num_actions, p=action_probs) # Take the chosen action and observe the next state and reward next_state, reward, done, _ = env.step(action) # Store the current state, action, and reward states.append(state) actions.append(action) rewards.append(reward) #