Upload MSK-Bench data

.gitattributes

@@ -33,3 +33,130 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_balance_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_balance_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_balance_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_bunny_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_bunny_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_bunny_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_catch_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_catch_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_catch_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_chinup_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_chinup_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_chinup_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_crawl_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_crawl_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_crawl_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_door_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_door_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_door_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_hurdle_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_hurdle_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_maze_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_maze_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_maze_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_pole_walk_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_pole_walk_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_powerlift_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_powerlift_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_powerlift_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_reach_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_reach_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_run_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_run_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_run_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_sidestep_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_sidestep_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_sidestep_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_sit_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_sit_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_sit_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_slidehard_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_slidehard_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_slidehard_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_squat_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_squat_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_squat_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stair_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stair_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stair_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stance_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stance_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stepping_stones_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stepping_stones_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_stepping_stones_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_walkandsit_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_walkandsit_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/ppo/ppo_walkandsit_checkpoints/eval_ppo_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_balance_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_balance_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_balance_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_bunnyhop_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_bunnyhop_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_bunnyhop_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_catch_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_catch_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_catch_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_chinup_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_chinup_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_chinup_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_crawl_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_crawl_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_crawl_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_door_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_door_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_door_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_hurdle_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_hurdle_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_hurdle_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_maze_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_maze_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_maze_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_pole_walk_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_pole_walk_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_pole_walk_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_powerlift_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_powerlift_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_powerlift_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_reach_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_reach_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_reach_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_run_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_run_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_run_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_sidestep_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_sidestep_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_sidestep_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_singlelegstance_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_singlelegstance_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_singlelegstance_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_sit_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_sit_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_sit_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_slidehard_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_slidehard_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_slidehard_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_squat_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_squat_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_squat_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_stair_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_stair_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_stair_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_stepping_stones_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_stepping_stones_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_stepping_stones_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_walkandsit_checkpoints/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_walkandsit_checkpoints/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/sac/sac_walkandsit_checkpoints/eval_sac_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/stair/260202.174137/EMG_plot_recfem_r.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/stair/260202.174137/eval_deprl_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/stair/260202.174137/eval_deprl_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/stair/260202.174137/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/stair/260202.174137/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/run/260502.131631/eval_deprl_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/run/260502.131631/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/run/260502.131631/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/walk/260521.131030/eval_deprl_best_energy_plot.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/walk/260521.131030/eval_Joint_jitter_report.png filter=lfs diff=lfs merge=lfs -text
+data/deprl/walk/260521.131030/eval_Muscle_jitter_report.png filter=lfs diff=lfs merge=lfs -text

data/README.md

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+# MSK-Bench Weights and Experiment Artifacts
+
+The `data/` directory contains policy weights for MyoSuite/MSK-Bench tasks, together with their configuration files, training logs, and evaluation results. The top-level folders identify algorithms or experiment series. Weights from different folders are not interchangeable, even when their task names look similar.
+
+## Where to find weights
+
+| Directory | Contents | Start with |
+| --- | --- | --- |
+| [`deprl/`](./deprl/) | DEPRL walking, running, and stair-climbing baselines; the configurations use DEP + MPO | `walk/`, `run/`, or `stair/` → `checkpoints/best.pt` |
+| [`msgym/`](./msgym/) | `SAC_DynSyn` policies organized by MyoSuite environment and experiment run | `<environment>/<run>/checkpoint/best_model.zip` |
+| [`ppo/`](./ppo/) | PPO weights, normalization state, evaluation reports, and training logs | `ppo_<task>_checkpoints/best_model.zip` |
+| [`residualrl/`](./residualrl/) | ResidualRL walking, running, and stair-climbing experiments; the configurations use DEP + MPO | `<task>/checkpoints/best.pt` |
+| [`sac/`](./sac/) | SAC weights, normalization state, evaluation reports, and training logs | `sac_<task>_checkpoints/best_model.zip` |
+
+The word `best` is part of the original filenames; this README does not infer which metric was used to select those checkpoints. To reproduce an evaluation, also check the configuration, environment version, and any associated preprocessing or normalization state.
+
+## `deprl/`: DEPRL baselines
+
+This directory has three task folders. Each numbered subdirectory contains one experiment run. The environment names below come from the corresponding `config.yaml` files.
+
+| Subdirectory | Environment in the configuration | Purpose |
+| --- | --- | --- |
+| `walk/260521.131030/` | `myoLegWalk-v0` | Leg-walking policy and evaluation results |
+| `run/260502.131631/` | `MyoRun-v0` | Running policy and evaluation results |
+| `stair/260202.174137/` | `myoLegStair-v0` | Stair-climbing policy and evaluation results |
+
+A run typically contains:
+
+- `config.yaml`: environment, DEP, MPO, training, and checkpoint settings.
+- `script.py`: the saved training script, useful when inspecting or reproducing the experiment.
+- `checkpoints/best.pt`: the checkpoint labeled best; `step_*.pt` files are checkpoints saved at specific training steps.
+- `checkpoints/logger.pt` and `checkpoints/time.pt`: saved logging or training-progress state, not standalone policy weights.
+- `log.csv`: training and test metrics.
+- `eval_*.png` and `*_compliance_report.csv`: plots and numerical reports for energy, jitter, muscle actions, and physical joints.
+- `EMG_*`: electromyography-related results, where present.
+
+## `msgym/`: Policies grouped by environment and run
+
+The first level is a MyoSuite environment, such as `myoLegWalk-v0/` or `MyoRun-v0/`. The next level, such as `0625-005737_0/`, contains the artifacts from one run. Some environments have several runs; keep a model with the configuration and environment state from the same run. All current run configuration JSON files identify the agent as `SAC_DynSyn`.
+
+Environment folders:
+
+```text
+MyoBunnyHop-v0       MyoCatch-v0            MyoChinUp-v0
+MyoCrawl-v0          MyoDoorOpen-v0         MyoHurdle-v0
+myoLegBalance-v0     myoLegSquat-v0         myoLegStair-v0
+myoLegStandRandom-v0 myoLegWalk-v0          MyoMazeWalk-v0
+MyoPoleWalk-v0       myoPowerlift-v0        MyoRun-v0
+MyoSidestep-v0       MyoSingleLegStance-v0  MyoSteppingStones-v0
+myoWalkAndSit-v0
+```
+
+Common files in a run:
+
+| Path or file | Purpose |
+| --- | --- |
+| `<task>.json` | Algorithm, environment, network, and training parameters; the filename varies by task. |
+| `checkpoint/best_model.zip` and `final_model.zip` | Stable-Baselines3 policy archives labeled best and final by the original experiment. Some runs may contain only one of them. |
+| `checkpoint/best_env.zip` and `final_env.zip` | Environment state saved alongside the models. **Despite their `.zip` extensions, these are Python pickle streams, not ordinary ZIP archives.** Load them using the original experiment's code. |
+| `eval/evaluations.npz` | Periodic evaluation data in NumPy format. |
+| `monitor/*.monitor.csv` | Episode records, including reward `r`, length `l`, and time `t`. |
+| `events.out.tfevents.*` | TensorBoard event logs. |
+
+## `ppo/` and `sac/`: Task-specific checkpoints and logs
+
+Both directories pair a `<algorithm>_<task>_checkpoints/` folder (models and evaluation results) with a `<algorithm>_<task>_logs/` folder (evaluation and training logs). For example, `ppo_balance_checkpoints/` pairs with `ppo_balance_logs/`. SAC uses the `sac_` prefix.
+
+Current task suffixes in `ppo/`:
+
+```text
+balance, bunny, catch, chinup, crawl, door, hurdle, maze, pole_walk,
+powerlift, reach, run, sidestep, sit, slidehard, squat, stair, stance,
+stepping_stones, walkandsit
+```
+
+Current task suffixes in `sac/`:
+
+```text
+balance, bunnyhop, catch, chinup, crawl, door, hurdle, maze, pole_walk,
+powerlift, reach, run, sidestep, singlelegstance, sit, slidehard, squat,
+stair, stepping_stones, walkandsit
+```
+
+These suffixes preserve the original directory names. The two algorithms do not have exactly the same task set or spelling. Key files in each `*_checkpoints/` folder are:
+
+- `best_model.zip`: a Stable-Baselines3 policy archive.
+- `vec_normalize.pkl`: vector-environment normalization state associated with that task's model; check and use the matching file when evaluating.
+- `eval_*_energy_plot.png` and `eval_*_jitter_report.png`: energy and jitter evaluation plots.
+- `Muscle_Actions_compliance_report.csv` and `Physical_Joints_compliance_report.csv`: per-dimension muscle-action and joint statistics, including derivative-energy and jerk fields.
+
+The matching `*_logs/` folder typically contains `eval_logs/evaluations.npz` (evaluation data) and `events.out.tfevents.*` under `PPO_1/` or `SAC_1/` (TensorBoard training logs).
+
+## `residualrl/`: ResidualRL experiments
+
+The `run/`, `stair/`, and `walk/` folders correspond to `MyoRunResidual-v0`, `MyoStairResidual-v0`, and `MyoWalkResidual-v0` in their configurations. Each task folder contains:
+
+- `config.yaml`: environment, DEP, MPO, and training parameters.
+- `script.py`: the saved training script.
+- `checkpoints/best.pt`: the checkpoint labeled best.
+- `checkpoints/step_*.pt`: checkpoints saved at specific training steps; `walk/` retains more intermediate steps.
+- `checkpoints/logger.pt` and `checkpoints/time.pt`: logging or training-progress state.
+- `log.csv`: training and test metrics.
+
+## Keeping files together
+
+1. Choose the **algorithm and environment/task** first, then select a weight file inside that directory. Similar task names do not make weights from different algorithms interchangeable.
+2. For `ppo/` and `sac/`, check `best_model.zip` together with the task's `vec_normalize.pkl`. For `msgym/`, keep the model with the JSON configuration and environment state from the same run.
+3. Loading `.pt`, `.pkl`, or `msgym`'s `*_env.zip` files may require the corresponding Python classes and library versions. These are experiment artifacts; the weight files alone are not guaranteed to provide a working inference setup.
+4. `log.csv`, `*.npz`, `*.monitor.csv`, `events.out.tfevents.*`, and the evaluation plots are for inspecting training or comparing experiments. They are not policy weights.
+

data/deprl/run/260502.131631/Muscle_Actions_compliance_report.csv

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+Dimension,1st_Deriv_Energy,2nd_Deriv_Energy (Accel),3rd_Deriv_Energy (Jerk),Max_Jerk_Peak,Mean_Abs_Jerk
+Muscle_Actions_0,326.1637,3.297185e+06,7.966307e+10,2.2571282e+06,74337.734
+Muscle_Actions_1,402.88864,2.921874e+06,5.53564e+10,1.3566284e+06,97847.51
+Muscle_Actions_2,714.9652,9.024319e+06,2.032388e+11,2.927109e+06,168837.23
+Muscle_Actions_3,367.54446,3.212593e+06,6.75701e+10,1.619199e+06,128155.35
+Muscle_Actions_4,311.82068,2.2097905e+06,3.2319588e+10,1.4490299e+06,68394.86
+Muscle_Actions_5,475.0835,4.992038e+06,1.1849997e+11,2.2255035e+06,128552.305
+Muscle_Actions_6,673.7217,6.277083e+06,1.3807636e+11,2.4065295e+06,150220.39
+Muscle_Actions_7,436.66382,4.4395165e+06,9.4105895e+10,2.3359345e+06,106341.86
+Muscle_Actions_8,348.83432,2.6533788e+06,3.7075087e+10,1.3427528e+06,75460.05
+Muscle_Actions_9,265.72687,2.2742322e+06,4.911947e+10,1.881929e+06,61514.86
+Muscle_Actions_10,552.3184,4.237859e+06,7.0646596e+10,1.6236234e+06,108177.04
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data/deprl/run/260502.131631/Physical_Joints_compliance_report.csv

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data/deprl/run/260502.131631/checkpoints/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8e43fa6ec9057c02d6bb5d0f865e5d2c90f55c0207bf75ed617a361742165b8
+size 151044310

data/deprl/run/260502.131631/checkpoints/logger.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2e8acf0db4a3c72874839bc9350d802f22a7aeff44c530d7b551255be37e5f6
+size 4380

data/deprl/run/260502.131631/checkpoints/step_100000000000000.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8e43fa6ec9057c02d6bb5d0f865e5d2c90f55c0207bf75ed617a361742165b8
+size 151044310

data/deprl/run/260502.131631/checkpoints/time.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d20acfd2a52424e76aa17f432e34e91064c564db3e8d17d98a9922a7159ce1a1
+size 852

data/deprl/run/260502.131631/config.yaml

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+DEP:
+  bias_rate: 0.002
+  buffer_size: 200
+  intervention_length: 5
+  intervention_proba: 0.0004
+  kappa: 1169.7
+  normalization: independent
+  q_norm_selector: l2
+  regularization: 16
+  s4avg: 2
+  sensor_delay: 1
+  tau: 40
+  test_episode_every: 3
+  time_dist: 5
+  with_learning: true
+env_args: {}
+mpo_args:
+  hidden_size: 2048
+  lr_actor: 3.53e-05
+  lr_critic: 6.081e-05
+  lr_dual: 0.00213
+tonic:
+  after_training: ''
+  agent: deprl.custom_agents.dep_factory(3, deprl.custom_mpo_torch.TunedMPO())(replay=deprl.replays.buffers.Buffer(return_steps=3,
+    batch_size=256, steps_between_batches=1000, batch_iterations=30, steps_before_batches=2e5))
+  before_training: ''
+  checkpoint: best
+  environment: deprl.environments.Gym('MyoRun-v0', scaled_actions=False, reset_type='none')
+  environment_name: deprl_run_baseline
+  full_save: 0
+  header: import deprl, myosuite; from myosuite.utils import gym
+  name: myoRun
+  parallel: 100
+  resume: 0
+  seed: 0
+  sequential: 10
+  test_environment: null
+  trainer: deprl.custom_trainer.Trainer(steps=int(2e8), epoch_steps=int(2e5), save_steps=int(1e6))
+working_dir: ./baselines_MyoRun

data/deprl/run/260502.131631/script.py

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+"""Script used to train agents."""
+import deprl
+import os
+import traceback
+
+
+import torch
+
+from deprl import custom_distributed
+from deprl.utils import load_checkpoint, prepare_params
+from deprl.vendor.tonic import logger
+
+
+def train(
+    config,
+):
+    """
+    Trains an agent on an environment.
+    """
+    tonic_conf = config["tonic"]
+
+    # Run the header first, e.g. to load an ML framework.
+    if "header" in tonic_conf:
+        exec(tonic_conf["header"])
+
+    # In case no env_args are passed via the config
+    if "env_args" not in config or config["env_args"] is None:
+        config["env_args"] = {}
+
+    # Build the training environment.
+    _environment = tonic_conf["environment"]
+    environment = custom_distributed.distribute(
+        environment=_environment,
+        tonic_conf=tonic_conf,
+        env_args=config["env_args"],
+    )
+    environment.initialize(seed=tonic_conf["seed"])
+
+    # Build the testing environment.
+    _test_environment = (
+        tonic_conf["test_environment"]
+        if "test_environment" in tonic_conf
+        and tonic_conf["test_environment"] is not None
+        else _environment
+    )
+    test_env_args = (
+        config["test_env_args"]
+        if "test_env_args" in config
+        else config["env_args"]
+    )
+    test_environment = custom_distributed.distribute(
+        environment=_test_environment,
+        tonic_conf=tonic_conf,
+        env_args=test_env_args,
+        parallel=1,
+        sequential=1,
+    )
+    test_environment.initialize(seed=tonic_conf["seed"] + 1000000)
+
+    # Build the agent.
+    if "agent" not in tonic_conf or tonic_conf["agent"] is None:
+        raise ValueError("No agent specified.")
+    agent = eval(tonic_conf["agent"])
+
+    # Set custom mpo parameters
+    if "mpo_args" in config:
+        agent.set_params(**config["mpo_args"])
+    agent.initialize(
+        observation_space=environment.observation_space,
+        action_space=environment.action_space,
+        seed=tonic_conf["seed"],
+    )
+
+    # Set DEP parameters
+    if hasattr(agent, "expl") and "DEP" in config:
+        agent.expl.set_params(config["DEP"])
+
+    # Initialize the logger to get paths
+    logger.initialize(
+        script_path=__file__,
+        config=config,
+        test_env=test_environment,
+        resume=tonic_conf["resume"],
+    )
+    path = logger.get_path()
+
+    # Process the checkpoint path same way as in tonic_conf.play
+    checkpoint_path = os.path.join(path, "checkpoints")
+
+    time_dict = {"steps": 0, "epochs": 0, "episodes": 0}
+    (
+        _,
+        checkpoint_path,
+        loaded_time_dict,
+    ) = load_checkpoint(checkpoint_path, checkpoint="last")
+    time_dict = time_dict if loaded_time_dict is None else loaded_time_dict
+
+    if checkpoint_path:
+        # Load the logger from a checkpoint.
+        logger.load(checkpoint_path, time_dict)
+        # Load the weights of the agent form a checkpoint.
+        agent.load(checkpoint_path)
+
+    # Build the trainer.
+    trainer = tonic_conf["trainer"] or "tonic_conf.Trainer()"
+    trainer = eval(trainer)
+    trainer.initialize(
+        agent=agent,
+        environment=environment,
+        test_environment=test_environment,
+        full_save=tonic_conf["full_save"],
+    )
+
+    # Run some code before training.
+    if tonic_conf["before_training"]:
+        exec(tonic_conf["before_training"])
+
+    # Train.
+    try:
+        trainer.run(config, **time_dict)
+    except Exception as e:
+        logger.log(f"trainer failed. Exception: {e}")
+        traceback.print_tb(e.__traceback__)
+
+    # Run some code after training.
+    if tonic_conf["after_training"]:
+        exec(["after_training"])
+
+
+def set_tensor_device():
+    # use CUDA or apple metal
+    if torch.cuda.is_available():
+        torch.set_default_device("cuda")
+        logger.log("CUDA detected, storing default tensors on it.")
+    elif torch.backends.mps.is_available():
+        torch.set_default_device("mps")
+        logger.log("MPS detected, storing default tensors on it.")
+    else:
+        logger.log("No CUDA or MPS detected, running on CPU")
+
+
+def main():
+    config = prepare_params()
+    if "cpu_override" in config["tonic"] and config["tonic"]["cpu_override"]:
+        torch.set_default_device("cpu")
+        logger.log("Manually forcing CPU run.")
+    else:
+        set_tensor_device()
+    print(config)
+    train(config)
+
+
+if __name__ == "__main__":
+    main()

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data/deprl/stair/260202.174137/Physical_Joints_compliance_report.csv

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data/deprl/stair/260202.174137/checkpoints/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:57e6ab07f9b21ba265d1c110e26a5caf5af9584180a2051d412a72c03e54a220
+size 151404886

data/deprl/stair/260202.174137/checkpoints/logger.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1878cff4040bc22a507ae57da5d8e21c19fe553f7f7794b8d18c2ea3d3322650
+size 4380

data/deprl/stair/260202.174137/checkpoints/step_150000000.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad9499b50d17e3efaa805ff194ddb0034fe0ccaf19f4b0f83db4f383bacb07bb
+size 151406890

data/deprl/stair/260202.174137/checkpoints/time.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4a854013c152781c595734470ec728c9d40791adee54c92163a89e2968ea794
+size 852

data/deprl/stair/260202.174137/config.yaml

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+DEP:
+  bias_rate: 0.002
+  buffer_size: 200
+  intervention_length: 5
+  intervention_proba: 0.0004
+  kappa: 1169.7
+  normalization: independent
+  q_norm_selector: l2
+  regularization: 16
+  s4avg: 2
+  sensor_delay: 1
+  tau: 40
+  test_episode_every: 3
+  time_dist: 5
+  with_learning: true
+env_args: {}
+mpo_args:
+  hidden_size: 2048
+  lr_actor: 2.0e-05
+  lr_critic: 6.081e-05
+  lr_dual: 0.00213
+tonic:
+  after_training: ''
+  agent: deprl.custom_agents.dep_factory(3, deprl.custom_mpo_torch.TunedMPO())(replay=deprl.replays.buffers.Buffer(return_steps=3,
+    batch_size=256, steps_between_batches=1000, batch_iterations=30, steps_before_batches=2e5))
+  before_training: ''
+  checkpoint: last
+  environment: deprl.environments.Gym('myoLegStair-v0', scaled_actions=False, reset_type='init')
+  environment_name: myo_stair_baseline
+  full_save: 0
+  header: import deprl, myosuite, gymnasium; from myosuite.utils import gym
+  name: myoStair_v1
+  parallel: 120
+  resume: 0
+  seed: 42
+  sequential: 10
+  trainer: deprl.custom_trainer.Trainer(steps=int(3e8), epoch_steps=int(2e5), save_steps=int(1e6))
+working_dir: ./baselines_Stair

data/deprl/stair/260202.174137/script.py

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+"""Script used to train agents."""
+import deprl
+import os
+import traceback
+
+
+import torch
+
+from deprl import custom_distributed
+from deprl.utils import load_checkpoint, prepare_params
+from deprl.vendor.tonic import logger
+
+
+def train(
+    config,
+):
+    """
+    Trains an agent on an environment.
+    """
+    tonic_conf = config["tonic"]
+
+    # Run the header first, e.g. to load an ML framework.
+    if "header" in tonic_conf:
+        exec(tonic_conf["header"])
+
+    # In case no env_args are passed via the config
+    if "env_args" not in config or config["env_args"] is None:
+        config["env_args"] = {}
+
+    # Build the training environment.
+    _environment = tonic_conf["environment"]
+    environment = custom_distributed.distribute(
+        environment=_environment,
+        tonic_conf=tonic_conf,
+        env_args=config["env_args"],
+    )
+    environment.initialize(seed=tonic_conf["seed"])
+
+    # Build the testing environment.
+    _test_environment = (
+        tonic_conf["test_environment"]
+        if "test_environment" in tonic_conf
+        and tonic_conf["test_environment"] is not None
+        else _environment
+    )
+    test_env_args = (
+        config["test_env_args"]
+        if "test_env_args" in config
+        else config["env_args"]
+    )
+    test_environment = custom_distributed.distribute(
+        environment=_test_environment,
+        tonic_conf=tonic_conf,
+        env_args=test_env_args,
+        parallel=1,
+        sequential=1,
+    )
+    test_environment.initialize(seed=tonic_conf["seed"] + 1000000)
+
+    # Build the agent.
+    if "agent" not in tonic_conf or tonic_conf["agent"] is None:
+        raise ValueError("No agent specified.")
+    agent = eval(tonic_conf["agent"])
+
+    # Set custom mpo parameters
+    if "mpo_args" in config:
+        agent.set_params(**config["mpo_args"])
+    agent.initialize(
+        observation_space=environment.observation_space,
+        action_space=environment.action_space,
+        seed=tonic_conf["seed"],
+    )
+
+    # Set DEP parameters
+    if hasattr(agent, "expl") and "DEP" in config:
+        agent.expl.set_params(config["DEP"])
+
+    # Initialize the logger to get paths
+    logger.initialize(
+        script_path=__file__,
+        config=config,
+        test_env=test_environment,
+        resume=tonic_conf["resume"],
+    )
+    path = logger.get_path()
+
+    # Process the checkpoint path same way as in tonic_conf.play
+    checkpoint_path = os.path.join(path, "checkpoints")
+
+    time_dict = {"steps": 0, "epochs": 0, "episodes": 0}
+    (
+        _,
+        checkpoint_path,
+        loaded_time_dict,
+    ) = load_checkpoint(checkpoint_path, checkpoint="last")
+    time_dict = time_dict if loaded_time_dict is None else loaded_time_dict
+
+    if checkpoint_path:
+        # Load the logger from a checkpoint.
+        logger.load(checkpoint_path, time_dict)
+        # Load the weights of the agent form a checkpoint.
+        agent.load(checkpoint_path)
+
+    # Build the trainer.
+    trainer = tonic_conf["trainer"] or "tonic_conf.Trainer()"
+    trainer = eval(trainer)
+    trainer.initialize(
+        agent=agent,
+        environment=environment,
+        test_environment=test_environment,
+        full_save=tonic_conf["full_save"],
+    )
+
+    # Run some code before training.
+    if tonic_conf["before_training"]:
+        exec(tonic_conf["before_training"])
+
+    # Train.
+    try:
+        trainer.run(config, **time_dict)
+    except Exception as e:
+        logger.log(f"trainer failed. Exception: {e}")
+        traceback.print_tb(e.__traceback__)
+
+    # Run some code after training.
+    if tonic_conf["after_training"]:
+        exec(["after_training"])
+
+
+def set_tensor_device():
+    # use CUDA or apple metal
+    if torch.cuda.is_available():
+        torch.set_default_device("cuda")
+        logger.log("CUDA detected, storing default tensors on it.")
+    elif torch.backends.mps.is_available():
+        torch.set_default_device("mps")
+        logger.log("MPS detected, storing default tensors on it.")
+    else:
+        logger.log("No CUDA or MPS detected, running on CPU")
+
+
+def main():
+    config = prepare_params()
+    if "cpu_override" in config["tonic"] and config["tonic"]["cpu_override"]:
+        torch.set_default_device("cpu")
+        logger.log("Manually forcing CPU run.")
+    else:
+        set_tensor_device()
+    print(config)
+    train(config)
+
+
+if __name__ == "__main__":
+    main()

data/deprl/walk/260521.131030/Muscle_Actions_compliance_report.csv

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+Muscle_Actions_4,801.9452,1.1263727e+07,3.163225e+11,3.3248152e+06,208499.7
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+Muscle_Actions_7,153.504,1.6504109e+06,3.7010604e+10,1.608076e+06,42833.344
+Muscle_Actions_8,0.01077225,211.0159,6.1058335e+06,29003.262,331.25558
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data/deprl/walk/260521.131030/Physical_Joints_compliance_report.csv

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data/deprl/walk/260521.131030/checkpoints/logger.pt

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data/deprl/walk/260521.131030/checkpoints/time.pt

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data/deprl/walk/260521.131030/config.yaml

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+DEP:
+  bias_rate: 0.002
+  buffer_size: 200
+  intervention_length: 5
+  intervention_proba: 0.0004
+  kappa: 1169.7
+  normalization: independent
+  q_norm_selector: l2
+  regularization: 16
+  s4avg: 2
+  sensor_delay: 1
+  tau: 40
+  test_episode_every: 3
+  time_dist: 5
+  with_learning: true
+env_args: {}
+mpo_args:
+  hidden_size: 2048
+  lr_actor: 3.53e-05
+  lr_critic: 6.081e-05
+  lr_dual: 0.00213
+tonic:
+  after_training: ''
+  agent: deprl.custom_agents.dep_factory(3, deprl.custom_mpo_torch.TunedMPO())(replay=deprl.replays.buffers.Buffer(return_steps=3,
+    batch_size=256, steps_between_batches=1000, batch_iterations=30, steps_before_batches=2e5))
+  before_training: ''
+  checkpoint: best
+  environment: deprl.environments.Gym('myoLegWalk-v0', scaled_actions=False, reset_type='random')
+  environment_name: deprl_baseline
+  full_save: 0
+  header: import deprl, myosuite; from myosuite.utils import gym
+  name: myoLeg
+  parallel: 100
+  resume: 0
+  seed: 0
+  sequential: 10
+  test_environment: null
+  trainer: deprl.custom_trainer.Trainer(steps=int(2e8), epoch_steps=int(2e5), save_steps=int(1e6))
+working_dir: ./baselines_DEPRL

data/deprl/walk/260521.131030/script.py

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+"""Script used to train agents."""
+import deprl
+import os
+import traceback
+
+
+import torch
+
+from deprl import custom_distributed
+from deprl.utils import load_checkpoint, prepare_params
+from deprl.vendor.tonic import logger
+
+
+def train(
+    config,
+):
+    """
+    Trains an agent on an environment.
+    """
+    tonic_conf = config["tonic"]
+
+    # Run the header first, e.g. to load an ML framework.
+    if "header" in tonic_conf:
+        exec(tonic_conf["header"])
+
+    # In case no env_args are passed via the config
+    if "env_args" not in config or config["env_args"] is None:
+        config["env_args"] = {}
+
+    # Build the training environment.
+    _environment = tonic_conf["environment"]
+    environment = custom_distributed.distribute(
+        environment=_environment,
+        tonic_conf=tonic_conf,
+        env_args=config["env_args"],
+    )
+    environment.initialize(seed=tonic_conf["seed"])
+
+    # Build the testing environment.
+    _test_environment = (
+        tonic_conf["test_environment"]
+        if "test_environment" in tonic_conf
+        and tonic_conf["test_environment"] is not None
+        else _environment
+    )
+    test_env_args = (
+        config["test_env_args"]
+        if "test_env_args" in config
+        else config["env_args"]
+    )
+    test_environment = custom_distributed.distribute(
+        environment=_test_environment,
+        tonic_conf=tonic_conf,
+        env_args=test_env_args,
+        parallel=1,
+        sequential=1,
+    )
+    test_environment.initialize(seed=tonic_conf["seed"] + 1000000)
+
+    # Build the agent.
+    if "agent" not in tonic_conf or tonic_conf["agent"] is None:
+        raise ValueError("No agent specified.")
+    agent = eval(tonic_conf["agent"])
+
+    # Set custom mpo parameters
+    if "mpo_args" in config:
+        agent.set_params(**config["mpo_args"])
+    agent.initialize(
+        observation_space=environment.observation_space,
+        action_space=environment.action_space,
+        seed=tonic_conf["seed"],
+    )
+
+    # Set DEP parameters
+    if hasattr(agent, "expl") and "DEP" in config:
+        agent.expl.set_params(config["DEP"])
+
+    # Initialize the logger to get paths
+    logger.initialize(
+        script_path=__file__,
+        config=config,
+        test_env=test_environment,
+        resume=tonic_conf["resume"],
+    )
+    path = logger.get_path()
+
+    # Process the checkpoint path same way as in tonic_conf.play
+    checkpoint_path = os.path.join(path, "checkpoints")
+
+    time_dict = {"steps": 0, "epochs": 0, "episodes": 0}
+    (
+        _,
+        checkpoint_path,
+        loaded_time_dict,
+    ) = load_checkpoint(checkpoint_path, checkpoint="last")
+    time_dict = time_dict if loaded_time_dict is None else loaded_time_dict
+
+    if checkpoint_path:
+        # Load the logger from a checkpoint.
+        logger.load(checkpoint_path, time_dict)
+        # Load the weights of the agent form a checkpoint.
+        agent.load(checkpoint_path)
+
+    # Build the trainer.
+    trainer = tonic_conf["trainer"] or "tonic_conf.Trainer()"
+    trainer = eval(trainer)
+    trainer.initialize(
+        agent=agent,
+        environment=environment,
+        test_environment=test_environment,
+        full_save=tonic_conf["full_save"],
+    )
+
+    # Run some code before training.
+    if tonic_conf["before_training"]:
+        exec(tonic_conf["before_training"])
+
+    # Train.
+    try:
+        trainer.run(config, **time_dict)
+    except Exception as e:
+        logger.log(f"trainer failed. Exception: {e}")
+        traceback.print_tb(e.__traceback__)
+
+    # Run some code after training.
+    if tonic_conf["after_training"]:
+        exec(["after_training"])
+
+
+def set_tensor_device():
+    # use CUDA or apple metal
+    if torch.cuda.is_available():
+        torch.set_default_device("cuda")
+        logger.log("CUDA detected, storing default tensors on it.")
+    elif torch.backends.mps.is_available():
+        torch.set_default_device("mps")
+        logger.log("MPS detected, storing default tensors on it.")
+    else:
+        logger.log("No CUDA or MPS detected, running on CPU")
+
+
+def main():
+    config = prepare_params()
+    if "cpu_override" in config["tonic"] and config["tonic"]["cpu_override"]:
+        torch.set_default_device("cpu")
+        logger.log("Manually forcing CPU run.")
+    else:
+        set_tensor_device()
+    print(config)
+    train(config)
+
+
+if __name__ == "__main__":
+    main()

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+{
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+    "agent_kwargs": {
+        "learning_rate": "linear_schedule(0.0005)",
+        "gradient_steps": 1,
+        "buffer_size": 1000000,
+        "policy_kwargs": {
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+            "use_sde": false
+        },
+        "dynsyn_k": 5e-9,
+        "dynsyn_a": 3e7
+    },
+    "env_name": "MyoBunnyHop-v0",
+    "env_nums": 64,
+    "single_env_kwargs": {
+        "skip_frames": 10,
+        "reset_noise_scale": 0.001,
+        "qpos_diff_th": 0.06,
+        "gait_cycles": 3,
+        "random_init": true,
+        "reward_dict": {
+            "w_qpos": 50,
+            "w_xpos": 50,
+            "w_pelvis": 100,
+            "w_energy": 0.1,
+            "w_healthy": 100
+        }
+    },
+    "wrapper_list": {
+        "MuscleNormWrapper": {}
+    },
+    "vec_normalize": {
+        "is_norm": true,
+        "kwargs": {
+            "norm_obs": true,
+            "norm_reward": false,
+            "clip_obs": 10.0
+        }
+    },
+    "total_timesteps": 100000000,
+    "play": false,
+    "load_model_dir": "",
+    "check_freq": 5000000,
+    "record_freq": 5000000,
+    "dump_freq": 0,
+    "reward_freq": 5000,
+    "log_root_dir": "/DATA_EDS/tao.meng.2604/msgym_logs",
+    "seed": 0
+}

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