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REINFORCE: Generalize for any environment #1265

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REINFORCE: Generalize for any environment #1265

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13 changes: 6 additions & 7 deletions reinforcement_learning/reinforce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,20 +19,20 @@
help='render the environment')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='interval between training status logs (default: 10)')
parser.add_argument('--env-id', type=str, default='CartPole-v1')
args = parser.parse_args()


env = gym.make('CartPole-v1')
env = gym.make(args.env_id)
env.reset(seed=args.seed)
torch.manual_seed(args.seed)


class Policy(nn.Module):
def __init__(self):
def __init__(self, n_observation, n_actions):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine1 = nn.Linear(n_observation, 128)
self.dropout = nn.Dropout(p=0.6)
self.affine2 = nn.Linear(128, 2)
self.affine2 = nn.Linear(128, n_actions)

self.saved_log_probs = []
self.rewards = []
Expand All @@ -44,8 +44,7 @@ def forward(self, x):
action_scores = self.affine2(x)
return F.softmax(action_scores, dim=1)


policy = Policy()
policy = Policy(env.observation_space.shape[0], env.action_space.n)
optimizer = optim.Adam(policy.parameters(), lr=1e-2)
eps = np.finfo(np.float32).eps.item()

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