Improve README, minor changes in procedural example (#1068)

README.md

@@ -6,10 +6,10 @@
 
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-> ⚠️️ **Dropped support of Gym**:
+> ⚠️️ **Dropped support for Gym**:
-> Tianshou no longer supports `gym`, and we recommend that you transition to 
+> Tianshou no longer supports Gym, and we recommend that you transition to 
 > [Gymnasium](http://github.com/Farama-Foundation/Gymnasium).
-> If you absolutely have to use gym, you can try using [Shimmy](https://github.com/Farama-Foundation/Shimmy) 
+> If you absolutely have to use Gym, you can try using [Shimmy](https://github.com/Farama-Foundation/Shimmy) 
 > (the compatibility layer), but Tianshou provides no guarantees that things will work then.
 
 > ⚠️️ **Current Status**: the Tianshou master branch is currently under heavy development,
@@ -179,7 +179,7 @@ Find example scripts in the [test/](https://github.com/thu-ml/tianshou/blob/mast
 
 <sup>(4): super fast APPO!</sup>
 
-### High quality software engineering standard
+### High Software Engineering Standards
 
 | RL Platform                                                        | Documentation                                                                                                                                                        | Code Coverage                                                                                                                                                                                                                                                                                                                                 | Type Hints         | Last Update                                                                                                       |
 | ------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ----------------------------------------------------------------------------------------------------------------- |
@@ -233,8 +233,6 @@ We shall apply the deep Q network (DQN) learning algorithm using both APIs.
 
 ### High-Level API
 
-The high-level API requires the extra package `argparse` (by adding 
-`--extras argparse`) to be installed.
 To get started, we need some imports.
 
 ```python
@@ -333,11 +331,15 @@ Here's a run (with the training time cut short):
   <img src="docs/_static/images/discrete_dqn_hl.gif">
 </p>
 
+Find many further applications of the high-level API in the `examples/` folder;
+look for scripts ending with `_hl.py`.
+Note that most of these examples require the extra package `argparse` 
+(install it by adding `--extras argparse` when invoking poetry).
 
 ### Procedural API
 
 Let us now consider an analogous example in the procedural API. 
-Find the full script from which the snippets below were derived at [test/discrete/test_dqn.py](https://github.com/thu-ml/tianshou/blob/master/test/discrete/test_dqn.py).
+Find the full script in [examples/discrete/discrete_dqn.py](https://github.com/thu-ml/tianshou/blob/master/examples/discrete/discrete_dqn.py).
 
 First, import some relevant packages:
 
@@ -358,24 +360,30 @@ gamma, n_step, target_freq = 0.9, 3, 320
 buffer_size = 20000
 eps_train, eps_test = 0.1, 0.05
 step_per_epoch, step_per_collect = 10000, 10
-logger = ts.utils.TensorboardLogger(SummaryWriter('log/dqn'))  # TensorBoard is supported!
+```
-# For other loggers: https://tianshou.readthedocs.io/en/master/01_tutorials/05_logger.html
+
+Initialize the logger:
+
+```python
+logger = ts.utils.TensorboardLogger(SummaryWriter('log/dqn'))
+# For other loggers, see https://tianshou.readthedocs.io/en/master/01_tutorials/05_logger.html
 ```
 
 Make environments:
 
 ```python
-# you can also try with SubprocVectorEnv
+# You can also try SubprocVectorEnv, which will use parallelization
 train_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(train_num)])
 test_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(test_num)])
 ```
 
-Define the network:
+Create the network as well as its optimizer:
 
 ```python
 from tianshou.utils.net.common import Net
-# you can define other net by following the API:
+
-# https://tianshou.readthedocs.io/en/master/01_tutorials/00_dqn.html#build-the-network
+# Note: You can easily define other networks.
+# See https://tianshou.readthedocs.io/en/master/01_tutorials/00_dqn.html#build-the-network
 env = gym.make(task, render_mode="human")
 state_shape = env.observation_space.shape or env.observation_space.n
 action_shape = env.action_space.shape or env.action_space.n
@@ -383,7 +391,7 @@ net = Net(state_shape=state_shape, action_shape=action_shape, hidden_sizes=[128,
 optim = torch.optim.Adam(net.parameters(), lr=lr)
 ```
 
-Setup policy and collectors:
+Set up the policy and collectors:
 
 ```python
 policy = ts.policy.DQNPolicy(
@@ -419,14 +427,14 @@ result = ts.trainer.OffpolicyTrainer(
 print(f"Finished training in {result.timing.total_time} seconds")
 ```
 
-Save / load the trained policy (it's exactly the same as PyTorch `nn.module`):
+Save/load the trained policy (it's exactly the same as loading a `torch.nn.module`):
 
 ```python
 torch.save(policy.state_dict(), 'dqn.pth')
 policy.load_state_dict(torch.load('dqn.pth'))
 ```
 
-Watch the performance with 35 FPS:
+Watch the agent with 35 FPS:
 
 ```python
 policy.eval()
@@ -435,13 +443,13 @@ collector = ts.data.Collector(policy, env, exploration_noise=True)
 collector.collect(n_episode=1, render=1 / 35)
 ```
 
-Look at the result saved in tensorboard: (with bash script in your terminal)
+Inspect the data saved in TensorBoard:
 
 ```bash
 $ tensorboard --logdir log/dqn
 ```
 
-You can check out the [documentation](https://tianshou.readthedocs.io) for advanced usage.
+Please read the [documentation](https://tianshou.readthedocs.io) for advanced usage.
 
 ## Contributing
 

examples/discrete/discrete_dqn.py

@@ -1,11 +1,8 @@
-from typing import cast
-
 import gymnasium as gym
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
 import tianshou as ts
-from tianshou.utils.space_info import SpaceInfo
 
 
 def main() -> None:
@@ -16,22 +13,21 @@ def main() -> None:
     buffer_size = 20000
     eps_train, eps_test = 0.1, 0.05
     step_per_epoch, step_per_collect = 10000, 10
-    logger = ts.utils.TensorboardLogger(SummaryWriter("log/dqn"))  # TensorBoard is supported!
-    # For other loggers: https://tianshou.readthedocs.io/en/master/tutorials/logger.html
 
-    # you can also try with SubprocVectorEnv
+    logger = ts.utils.TensorboardLogger(SummaryWriter("log/dqn"))  # TensorBoard is supported!
+    # For other loggers, see https://tianshou.readthedocs.io/en/master/tutorials/logger.html
+
+    # You can also try SubprocVectorEnv, which will use parallelization
     train_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(train_num)])
     test_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(test_num)])
 
     from tianshou.utils.net.common import Net
 
-    # you can define other net by following the API:
+    # Note: You can easily define other networks.
-    # https://tianshou.readthedocs.io/en/master/tutorials/dqn.html#build-the-network
+    # See https://tianshou.readthedocs.io/en/master/01_tutorials/00_dqn.html#build-the-network
     env = gym.make(task, render_mode="human")
-    env.action_space = cast(gym.spaces.Discrete, env.action_space)
+    state_shape = env.observation_space.shape or env.observation_space.n
-    space_info = SpaceInfo.from_env(env)
+    action_shape = env.action_space.shape or env.action_space.n
-    state_shape = space_info.observation_info.obs_shape
-    action_shape = space_info.action_info.action_shape
     net = Net(state_shape=state_shape, action_shape=action_shape, hidden_sizes=[128, 128, 128])
     optim = torch.optim.Adam(net.parameters(), lr=lr)