amend

docs/source/index.rst

@@ -65,6 +65,11 @@ or via a ``git clone`` if you're willing to contribute to the library:
 Getting started
 ===============
 
+A series of quick tutorials to get ramped up with the basic features of the
+library. If you're in a hurry, you can start by
+:ref:`the last item of the series <gs_first_training>`
+and navigate to the previous ones whenever you want to learn more!
+
 .. toctree::
    :maxdepth: 1
 

torchrl/record/loggers/csv.py

@@ -2,6 +2,8 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
+from __future__ import annotations
+
 import os
 from collections import defaultdict
 from pathlib import Path
@@ -126,7 +128,7 @@ class CSVLogger(Logger):
     def __init__(
         self,
         exp_name: str,
-        log_dir: Optional[str] = None,
+        log_dir: str | None = None,
         video_format: str = "pt",
         video_fps: int = 30,
     ) -> None:

tutorials/sphinx-tutorials/getting-started-5.py

@@ -27,6 +27,8 @@
 # transform. These features are presented in
 # :ref:`the environment tutorial <gs_env_ted>`.
 #
+import pathlib
+
 import torch
 
 torch.manual_seed(0)
@@ -76,7 +78,13 @@
 init_rand_steps = 5000
 frames_per_batch = 100
 optim_steps = 10
-collector = SyncDataCollector(env, policy, frames_per_batch=frames_per_batch, total_frames=-1, init_random_frames=init_rand_steps)
+collector = SyncDataCollector(
+    env,
+    policy,
+    frames_per_batch=frames_per_batch,
+    total_frames=-1,
+    init_random_frames=init_rand_steps,
+)
 rb = ReplayBuffer(storage=LazyTensorStorage(100_000))
 
 from torch.optim import Adam
@@ -94,9 +102,19 @@
 #################################
 # Logger
 # ------
-# TODO
+#
+# We'll be using a CSV logger to log our results, and save rendered videos.
+#
 
 from torchrl._utils import logger as torchrl_logger
+from torchrl.record import CSVLogger, VideoRecorder
+
+path = pathlib.Path(__file__).parent / "training_loop"
+logger = CSVLogger(exp_name="dqn", log_dir=path, video_format="mp4")
+video_recorder = VideoRecorder(logger, tag="video")
+record_env = TransformedEnv(
+    GymEnv("CartPole-v1", from_pixels=True, pixels_only=False), video_recorder
+)
 
 #################################
 # Training loop
@@ -134,3 +152,18 @@
 torchrl_logger.info(
     f"solved after {total_count} steps, {total_episodes} episodes and in {t1-t0}s."
 )
+
+#################################
+# Rendering
+# ---------
+#
+# Finally, we run the environment for as many steps as we can and save the
+# video locally (notice that we are not exploring).
+
+record_env.rollout(max_steps=1000, policy=policy)
+video_recorder.dump()
+
+#################################
+#
+# .. figure:: ./training_loop/dqn/videos/video_0.mp4
+#