Question about the paper/implementation

[araffin]

Hello, thanks for sharing and open sourcing the work. After a quick read of the paper, I had several questions:

• did you do an ablation of UTD? in my experiments, UTD=10 may already be enough (at least with TQC, see below) and one major detail is the policy delay (as done in REDQ or DROPQ)
• did you consider using TQC ? (SAC + distributional critic, it may remove the number of multiple critics too and usually yields better resuls than SAC, see https://sb3-contrib.readthedocs.io/en/master/modules/tqc.html#results and https://github.com/SamsungLabs/tqc_pytorch)
• are you using a low-pass filter on the real robot? Have you considered not using one as in https://proceedings.mlr.press/v164/raffin22a.html? (also learning directly on real robot: https://www.youtube.com/watch?v=f_FmDFrYkPM) or how do you ensure you are not breaking the robot by sending high-frequency commands (with larger value in motor damping?)

I have a working implementation of TQC + DropQ using Stable-Baselines3 that I can also share ;) (I can do a PR on request, and it will probably part of SB3 soon) SB3 branch: https://github.com/DLR-RM/stable-baselines3/tree/feat/dropq SB3 contrib branch: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib/tree/feat/dropq Training script: https://github.com/araffin/walk_in_the_park/blob/feat/sb3/train_sb3.py

EDIT: SBX = SB3 + Jax is available here: https://github.com/araffin/sbx (with TQC, DroQ and SAC-N)

W&B example run: https://wandb.ai/araffin/a1/runs/2ln32rqx?workspace=user-araffin

[ikostrikov]

Hello,

• we ablated over different utds and found that utd=20 works best. See this figure.
• TQC is an exciting algorithm. However, we didn't try it specifically for this work.
• we ran experiments with and without a low pass filter; however, in our specific setup, we didn't notice a significant difference, probably due to larger damping values. At the same time, I think in many scenarios the low-pass filter can be useful.

Results for TQC+DroQ look interesting! However, we do not plan to expand this repository and intent to keep it frozen to ensure the reproducibility of the results reported in the paper.

[araffin]

Thanks for the swift answer =)

we ablated over different utds and found that utd=20 works best. See this figure.

given how fast is the implementation, it would make sense to even try UTD > 20, no?

Btw, what makes it so fast? jax only or additional special tricks?

Did you consider running the training for longer than 20 minutes or does it plateau/breaks? (let's say 1h for the easiest setup) Because the learned policies walk forward but one can tell it's a RL controller... (gaits are not so natural/good looking)

[ikostrikov]

Our laptop could run training only with utd=20 in real time, so we didn't try larger values :)

Yes, it's just jax.jit. Otherwise, it's a vanilla implementation without any additional engineering.

In the wild, we were constrained by the battery capacity :) With more training it gets better and better.

[araffin]

In the wild, we were constrained by the battery capacity :) With more training it gets better and better.

Alright... still curious to see what it could do in the simplest setting (indoor, no battery, flat ground).

fyi, I created a small report for the runs I did today with TQC ;) https://wandb.ai/araffin/a1/reports/TQC-with-DropQ-config-on-walk-in-the-park-env--VmlldzoyNTQxMzgz After minor tuning of the discount factor (gamma=0.98), it consistently reaches return > 3700 in only 8k env interactions =) (sometimes in only 5k)

[araffin]

As a follow up, I've got a working version of TQC + DroQ in jax here (borrowed some code from your implementation ;)): https://github.com/vwxyzjn/cleanrl/pull/272 (also a version of TQC + TD3 + DroQ, need to polish everything)

[i1Cps]

@araffin Morning, I was reading the paper and trying to understand where the simulations played a part in the training. I understood the paper was promoting the idea of training using the real environment over a simulation, cutting the steps sim -> real out.

But it then mentions using Mujoco and modelling the A1, I am assuming perhaps they initialised the training in mujoco then fine tuned with the real environment?

[araffin]

no no, they trained on the real robot only, i managed to reproduce the experiment: https://araffin.github.io/slides/design-real-rl-experiments/#/13/2

[i1Cps]

Nice work! But I'm then confused as to which way they Incorporated the simulation (specifically Mujoco) in there training/research. Was it simply to draw a comparison?