From 9caf7e27593c2b082ac41e223166760acd9e9557 Mon Sep 17 00:00:00 2001 From: Thomas Simonini Date: Tue, 17 Jan 2023 14:44:13 +0100 Subject: [PATCH] Update hands-on.mdx --- units/en/unit6/hands-on.mdx | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/units/en/unit6/hands-on.mdx b/units/en/unit6/hands-on.mdx index 7a043a4..37a0d93 100644 --- a/units/en/unit6/hands-on.mdx +++ b/units/en/unit6/hands-on.mdx @@ -153,6 +153,7 @@ print("Sample observation", env.observation_space.sample()) # Get a random obse ``` The observation Space (from [Jeffrey Y Mo](https://hackmd.io/@jeffreymo/SJJrSJh5_#PyBullet)): +The difference is that our observation space is 28 not 29. PyBullet Ant Obs space @@ -385,7 +386,7 @@ Now it's your turn: 2. Make a vectorized environment 3. Add a wrapper to normalize the observations and rewards. [Check the documentation](https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html#vecnormalize) 4. Create the A2C Model (don't forget verbose=1 to print the training logs). -5. Train it for 2M Timesteps +5. Train it for 1M Timesteps 6. Save the model and VecNormalize statistics when saving the agent 7. Evaluate your agent 8. Publish your trained model on the Hub 🔥 with `package_to_hub` @@ -445,7 +446,7 @@ package_to_hub( ## Some additional challenges 🏆 -The best way to learn **is to try things by your own**! Why not trying `HalfCheetahBulletEnv-v0` for PyBullet? +The best way to learn **is to try things by your own**! Why not trying `HalfCheetahBulletEnv-v0` for PyBullet and `PandaPickAndPlace-v1` for Panda-Gym? If you want to try more advanced tasks for panda-gym, you need to check what was done using **TQC or SAC** (a more sample-efficient algorithm suited for robotics tasks). In real robotics, you'll use a more sample-efficient algorithm for a simple reason: contrary to a simulation **if you move your robotic arm too much, you have a risk of breaking it**.