matsuolab / bremen Goto Github PK

Hi,

I tried to run deployment-efficient experiments to reproduce the results reported in the paper with the following command:

python recursive.py --env cheetah_run --exp_name recursive_example --sub_exp_name BREMEN_demo --param_path configs/params_cheetah_run_offline.json --bc_init --random_seeds 0 --target_kl 0.1 --max_path_length 250 --gaussian 0.1 --const_sampling

After the training is finished, I observed the following evaluation results:

---------------------------
| Iteration    | 399      |
| TotalSamples | 850000   |
| episode_max  | 745      |
| episode_mean | 741      |
| episode_min  | 735      |
---------------------------

However, according to Fig 2 in the original paper (https://arxiv.org/pdf/2006.03647.pdf), the performance of HalfCheetah should be around 6000, which is quite different from the evaluation results.

I wonder the parameter setting specified in the command above is the same as the setting of experiments in this paper? If not, could you let me know which hyper-parameter should be modified in order to reproduce the results reported in the paper? Or maybe there are some other reasons for this performance gap?

Thanks a lot!

Hi. Thanks for sharing the code. I am interested in offline reinforcement learning. In Appendix D. of the paper, you show the performance of BREMEN on D4RL, but the launch script is not found in the codebase. Do you have a plan to share the script to launch d4rl experiments?

Hello Authors!

Thanks so much for releasing this wonderful codebase. I have looked at your latest work on ICLR, and wondering if your code has the implementation on FetchReach-v0? Is it possible to also update the portion?

Thanks!
Candy

Hi. I noticed that there are cheetah run and half-cheetah environments in this repo.
What are the difference between the two environment?