README fixes and polish

README.md

@@ -16,6 +16,8 @@ Our repo provides you with the core algorithm and the following features:
 
 ### Installation on HoReKa
 
+Original Repo recommends ´uv´, but I prefer vanilla python and that seems to work...
+
 1. **Clone the repository and navigate to it:**
    ```bash
    git clone <repository-url>
@@ -42,22 +44,15 @@ The repository includes pre-configured SLURM scripts with wandb integration:
 
 #### Quick Start
 ```bash
-# Submit a ManiSkill job
-./submit_job.sh maniskill PickCube-v1 mjx_dmc_medium_data
-
-# Submit a Brax job  
 ./submit_job.sh brax ant mjx_dmc_small_data
 ```
 
 #### Manual Job Submission
 ```bash
-# Submit ManiSkill experiments
-sbatch slurm/run_reppo_maniskill.sh
-
-# Submit Brax experiments (completed)
+# Submit Brax experiments
 sbatch slurm/run_reppo_brax.sh
 
-# Submit DMC experiments (new)
+# Submit DMC experiments
 python submit_dmc_experiments.py --seeds 3
 
 # With custom environment
@@ -67,7 +62,7 @@ ENV_NAME=PlaceApple-v1 EXPERIMENT_TYPE=mjx_dmc_large_data sbatch slurm/run_reppo
 #### Supported Environments
 
 **ManiSkill environments:**
-- `PickCube-v1`, `PlaceApple-v1`, `StackCube-v1`, `PegInsertionSide-v1`
+- `PickCube-v1`, `PlaceApple-v1`, `StackCube-v1`, `PegInsertionSide-v1`, ...
 
 **Brax environments:**
 - `ant`, `cheetah`, `hopper`, `walker2d`, `humanoid`
@@ -87,11 +82,6 @@ tail -f logs/reppo_maniskill_<job_id>.out
 tail -f logs/reppo_brax_<job_id>.out
 ```
 
-All experiments automatically log to wandb with your configured credentials. Results will appear in projects:
-- `reppo_brax_production` (completed)  
-- `reppo_dmc_production` (in progress)
-- `reppo_maniskill` (pending)
-
 #### Critical Issues in Official Repository
 
 ⚠️ **The official REPPO repository is not runnable due to a series of fatal bugs.** These issues were discovered and fixed during HoReKa cluster deployment:
@@ -128,8 +118,6 @@ All experiments automatically log to wandb with your configured credentials. Res
 - **Root cause**: BraxGymnaxWrapper wasn't properly vectorized for multi-environment operations
 - **Fix applied**: Added proper vectorization support to `reset()` and `step()` methods using `jax.vmap` for handling both single and batched operations
 
-**Summary**: Fixed 6 critical bugs that prevented the original repository from running. The algorithm now successfully runs with 256 parallel environments and proper wandb integration, achieving strong learning performance (episode returns improving from ~-100 to ~400+ in ant environment).
-
 ---
 
 ## Original README