set policy.eval() before collector.collect (#204)

* fix #203

* no_grad argument in collector.collect

README.md

@@ -56,10 +56,8 @@ $ pip install tianshou
 You can also install with the newest version through GitHub:
 
 ```bash
-# latest release
+# latest version
 $ pip install git+https://github.com/thu-ml/tianshou.git@master
-# develop version
-$ pip install git+https://github.com/thu-ml/tianshou.git@dev
 ```
 
 If you use Anaconda or Miniconda, you can install Tianshou through the following command lines:

docs/contributing.rst

@@ -8,7 +8,6 @@ To install Tianshou in an "editable" mode, run
 
 .. code-block:: bash
 
-    $ git checkout dev
     $ pip install -e ".[dev]"
 
 in the main directory. This installation is removable by
@@ -70,9 +69,4 @@ To compile documentation into webpages, run
 
 under the ``docs/`` directory. The generated webpages are in ``docs/_build`` and can be viewed with browsers.
 
-Chinese documentation is in https://tianshou.readthedocs.io/zh/latest/, and the develop version of documentation is in https://tianshou.readthedocs.io/en/dev/.
-
-Pull Request
-------------
-
-All of the commits should merge through the pull request to the ``dev`` branch. The pull request must have 2 approvals before merging.
+Chinese documentation is in https://tianshou.readthedocs.io/zh/latest/.

docs/index.rst

@@ -46,10 +46,8 @@ You can also install with the newest version through GitHub:
 
 .. code-block:: bash
 
-    # latest release
+    # latest version
     $ pip install git+https://github.com/thu-ml/tianshou.git@master
-    # develop version
-    $ pip install git+https://github.com/thu-ml/tianshou.git@dev
 
 If you use Anaconda or Miniconda, you can install Tianshou through the following command lines:
 
@@ -70,7 +68,7 @@ After installation, open your python console and type
 
 If no error occurs, you have successfully installed Tianshou.
 
-Tianshou is still under development, you can also check out the documents in stable version through `tianshou.readthedocs.io/en/stable/ <https://tianshou.readthedocs.io/en/stable/>`_ and the develop version through `tianshou.readthedocs.io/en/dev/ <https://tianshou.readthedocs.io/en/dev/>`_.
+Tianshou is still under development, you can also check out the documents in stable version through `tianshou.readthedocs.io/en/stable/ <https://tianshou.readthedocs.io/en/stable/>`_.
 
 .. toctree::
    :maxdepth: 1

tianshou/data/collector.py

@@ -173,6 +173,7 @@ class Collector(object):
                 n_episode: Optional[Union[int, List[int]]] = None,
                 random: bool = False,
                 render: Optional[float] = None,
+                no_grad: bool = True,
                 ) -> Dict[str, float]:
         """Collect a specified number of step or episode.
 
@@ -185,6 +186,8 @@ class Collector(object):
             defaults to ``False``.
         :param float render: the sleep time between rendering consecutive
             frames, defaults to ``None`` (no rendering).
+        :param bool no_grad: whether to retain gradient in policy.forward,
+            defaults to ``True`` (no gradient retaining).
 
         .. note::
 
@@ -252,7 +255,10 @@ class Collector(object):
                 result = Batch(
                     act=[spaces[i].sample() for i in self._ready_env_ids])
             else:
-                with torch.no_grad():
+                if no_grad:
+                    with torch.no_grad():  # faster than retain_grad version
+                        result = self.policy(self.data, last_state)
+                else:
                     result = self.policy(self.data, last_state)
 
             state = result.get('state', Batch())

tianshou/trainer/offpolicy.py

@@ -76,13 +76,13 @@ def offpolicy_trainer(
     start_time = time.time()
     test_in_train = test_in_train and train_collector.policy == policy
     for epoch in range(1, 1 + max_epoch):
-        # train
-        policy.train()
-        if train_fn:
-            train_fn(epoch)
         with tqdm.tqdm(total=step_per_epoch, desc=f'Epoch #{epoch}',
                        **tqdm_config) as t:
             while t.n < t.total:
+                # collect
+                if train_fn:
+                    train_fn(epoch)
+                policy.eval()
                 result = train_collector.collect(n_step=collect_per_step)
                 data = {}
                 if test_in_train and stop_fn and stop_fn(result['rew']):
@@ -99,9 +99,10 @@ def offpolicy_trainer(
                             start_time, train_collector, test_collector,
                             test_result['rew'])
                     else:
-                        policy.train()
                         if train_fn:
                             train_fn(epoch)
+                # train
+                policy.train()
                 for i in range(update_per_step * min(
                         result['n/st'] // collect_per_step, t.total - t.n)):
                     global_step += collect_per_step

tianshou/trainer/onpolicy.py

@@ -76,13 +76,13 @@ def onpolicy_trainer(
     start_time = time.time()
     test_in_train = test_in_train and train_collector.policy == policy
     for epoch in range(1, 1 + max_epoch):
-        # train
-        policy.train()
-        if train_fn:
-            train_fn(epoch)
         with tqdm.tqdm(total=step_per_epoch, desc=f'Epoch #{epoch}',
                        **tqdm_config) as t:
             while t.n < t.total:
+                # collect
+                if train_fn:
+                    train_fn(epoch)
+                policy.eval()
                 result = train_collector.collect(n_episode=collect_per_step)
                 data = {}
                 if test_in_train and stop_fn and stop_fn(result['rew']):
@@ -99,9 +99,10 @@ def onpolicy_trainer(
                             start_time, train_collector, test_collector,
                             test_result['rew'])
                     else:
-                        policy.train()
                         if train_fn:
                             train_fn(epoch)
+                # train
+                policy.train()
                 losses = policy.update(
                     0, train_collector.buffer, batch_size, repeat_per_collect)
                 train_collector.reset_buffer()