Reinforcement Learning, Ciencia De Dados, Data Science, Machine Learning.
agente com política uniforme e aleatória: n_runs=200 cumulative_reward = [] for run in tqdm(range(n_runs)): terminal = False k=0 r = 0 while not terminal: k+=1 action = np.random.choice([0,1,2,3]) out = env.step(action) r+=out[1] terminal = out[2] cumulative_reward.append(r) print(f"run {run} levou