| 1) model = PPO(policy = "MlpPolicy", | |
| env = env, | |
| n_steps = 1024, | |
| batch_size = 64, | |
| n_epochs = 4, | |
| gamma = 0.999, | |
| gae_lambda = 0.98, | |
| ent_coef = 0.01, | |
| verbose=1) | |
| model.learn(total_timesteps = 500000) | |
| mean_reward=193.60 +/- 21.32519973099738 | |
| 2) model = PPO(policy = "MlpPolicy", | |
| env = env, | |
| n_steps = 1024, | |
| batch_size = 64, | |
| n_epochs = 8, | |
| gamma = 0.999, | |
| gae_lambda = 0.98, | |
| ent_coef = 0.01, | |
| verbose=1) | |
| model.learn(total_timesteps = 500000) | |
| mean_reward=235.09 +/- 21.878789192117072 | |
| 3) model = PPO(policy = "MlpPolicy", | |
| env = env, | |
| n_steps = 1024, | |
| batch_size = 64, | |
| n_epochs = 8, | |
| gamma = 0.999, | |
| gae_lambda = 0.98, | |
| ent_coef = 0.01, | |
| verbose=1) | |
| model.learn(total_timesteps = 1000000) | |