From cb1aff944107c07a9e00ac7e67dcc07442c01cbb Mon Sep 17 00:00:00 2001 From: Denis Yarats Date: Mon, 23 Sep 2019 15:21:50 -0400 Subject: [PATCH] Update README.md --- README.md | 43 ++----------------------------------------- 1 file changed, 2 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index 645843f..fa0bdf3 100644 --- a/README.md +++ b/README.md @@ -46,47 +46,8 @@ ALOSS - average loss of actor CLOSS - average loss of critic RLOSS - average reconstruction loss (only if is trained from pixels and decoder) ``` -and an evaluation entry : +while an evaluation entry: ``` | eval | S: 0 | ER: 21.1676 ``` -Which just tells the expected reward `ER` evaluating current policy after `S` steps. Note that `ER` is average evaluation performance over `num_eval_episodes` episodes (usually 10). - -## Running on the cluster -You can find the `run_cluster.sh` script file that allows you run training on the cluster. It is a simple bash script, that is super easy to modify. We usually run 10 different seeds for each configuration to get reliable results. For example to schedule 10 runs of `walker walk` simple do this: -``` -./run_cluster.sh walker walk -``` -This script will schedule 10 jobs and all the output will be stored under `./runs/walker_walk/{configuration_name}/seed_i`. The folder structure looks like this: -``` -runs/ - walker_walk/ - sac_states/ - seed_1/ - id # slurm job id - stdout # standard output of your job - stderr # standard error of your jobs - run.sh # starting script - run.slrm # slurm script - eval.log # log file for evaluation - train.log # log file for training - tb/ # folder that stores tensorboard output - video/ # folder stores evaluation videos - 10000.mp4 # video of one episode after 10000 steps - seed_2/ - ... -``` -Again, you can attach tensorboard to a particular configuration, for example: -``` -tensorboard --logdir runs/walker_walk/sac_states -``` - -For convinience, you can also use an iPython notebook to get aggregated over 10 seeds results. An example of such notebook is `runs.ipynb` - - -## Run entire testbed -Another scirpt that allow to run all 10 dm_control task on the cluster is here: -``` -./run_all.sh -``` -It will call `run_cluster.sh` for each task, so you only need to modify `run_cluster.sh` to change the hyper parameters. +which just tells the expected reward `ER` evaluating current policy after `S` steps. Note that `ER` is average evaluation performance over `num_eval_episodes` episodes (usually 10).