Documentation.

README.md

@@ -3,7 +3,7 @@
 > An 2D environment for training control policies for a quadrotor drone (technically a planar birotor). Rigth now, only Soft-Actor-Critic is implemented but I may revisit later (there are some failed PID and TD3 attempts :)).
 
 <span title="sac-agent-demo">
- <p align="left">
+ <p align="center">
   <img width="600vw" src="./assets/sac123k.gif" alt="sac-agent-demo">
  </p>
 </span>
@@ -14,7 +14,7 @@
 1. Create a virtual environment (I recommend `venv`).
 2. Run `.\venv\Scripts\activate` to enter the virtual environment
 3. Install the dependencies with `pip install -r requirements.txt`
-4. To train a new SAC policy from scratch, edit `src/run.py` so that `eval=False, load=False, render=False`. Training rewards will be logged and checkpoints will be saved periodically. Training can be further configured in `Agents/sac/run.py`.
+4. To train a new SAC policy from scratch, edit `src/main.py` so that `eval=False, load=False, render=False`. Training rewards will be logged and checkpoints will be saved periodically. Training can be further configured in `Agents/sac/run.py`.
 5. To load a model, place the 3 checkpoints in the `Agents/sac/load` directory and specify `load=True` in `run.py`.
 6. I've supplied my best weights (trained on cpu...) in releases.
 
@@ -27,6 +27,8 @@ The drone environment runs on Pygame and can be found in `drone/DroneEnv.py`. I
 - [ ] TD3
 - [ ] Prioritized Replay
 - [ ] LA3P
+- [ ] Obstacles and LiDaR simulation for POMDP (using an LSTM based network)
+- [ ] State estimation 
 
 
 ## References