REINFORCE: Generalize for any environment

- Parameterized by number of observations and actions

Author: slitvinov

reinforcement_learning/reinforce.py

@@ -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 = []
@@ -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()