Update README.md

README.md

@@ -4,8 +4,15 @@ A repository that contains example json files that can be used for different app
 
 Current available configs:
 
-env_config_so100.json: real robot environment configuration to be used to teleoperate, record dataset and replay a dataset on the real robot with the lerobot/scripts/rl/gym_manipulator.py script.
-train_config_hilserl_so100.json: training config on the real robot with the HILSerl RL framework in LeRobot.
-gym_hil_env.json: simulated environment configuration for the gym_hil mujoco based env.
-train_gym_hil_env.json: training configuration for gym_hil with the HILSerl RL framework in LeRobot.
-reward_classifier_train_config.json: configuration to train a reward classifier with LeRobot.
+  - RL :
+    - env_config.json: Environment config for a real robot setup using a gamepad for teleoperation and an SO10* arm as the main agent. Using `gym_manipulator.py`, one can use this config to teleoperate the robot and record a dataset for reinforcement learning.
+    - train_config.json: Training config for the HIL-SERL implementation in LeRobot on the real robot using the similar environment configuration to the `env_config.json` in the same directory. 
+    - gym_hil:
+      - env_config.json: Environment config for simulation using the `gym_hil` environment. 
+      - train_config.json: Training config for the HIL-SERL implementation in LeRobot for simulated environments.
+  - Sim IL:
+    - env_config.json: Environment config for simulation using the `gym_hil` environment. You can use this configuration to collect a dataset in simulation that can be used for imitation learning or reinforcement learning. 
+    - eval_config.json: Evaluation config for models trained on datasets collected from `gym_hil` environment.
+  - Reward Classifier:
+    - config.json: Main configuration for training a reward classifier.
+