Update README & environment

Author: Intelligent-Driving-Laboratory

README.md

@@ -3,14 +3,30 @@
 This repository accompanies [Reinforcement Learning for Sequential Decision and Optimal Control](https://www.link.springer.com/book/10.1007/9789811977831) by Shengbo Eben Li(SpringerNature, 2024).
 
 [comment]: #cover
-![Cover image]9789811977831.jpg)
+![Cover image](9789811977831.jpg)
 
 Download the files as a zip using the green button, or clone the repository to your machine using Git.
 
 ## Releases
 
 Release v1.0 corresponds to the code in the published book, without corrections or updates.
 
+## Run codes of this book
+Setup conda first, and install dependencies.
+```bash
+conda env create -n rlbook -f environment.yml
+conda activate rlbook
+```
+Then open each folder and run `main` or `plot` Python script.
+
+## Source Layout
+1. `Chap_3_4_CleanRobot`: Code for robot cleaning example in Chapter 3 and 4.
+2. `Chap_5_AutoCar_GridRoad`: Code for autonomous car example in Chapter 5.
+3. `Chap_6_Actor_Critic_Algorithm`: Code for actor-critic algorithm in Chapter 6.
+4. `Chap_7_AC_wtih_Baseline`: Code for AC algorithm with baseline comparison in Chapter 7.
+5. `Chap_8_Veh_Track_Ctrl`: Code for vehicle tracking control example in Chapter 8.
+6. `Chap_9_Car_Brake_Control`: Code for emergency braking control example in Chapter 9.
+
 ## Contributions
 
 See the file Contributing.md for more information on how you can contribute to this repository.

environment.yml

@@ -0,0 +1,17 @@
+channels:
+  - conda-forge
+dependencies:
+  - casadi=3.6.3=py39hc9cacc4_3
+  - libprotobuf=3.21.12=hfc55251_2
+  - matplotlib=3.5.3=py39hf3d152e_2
+  - numba=0.58.1=py39h615d6bd_0
+  - numpy=1.26.2=py39h474f0d3_0
+  - pandas=2.1.4=py39hddac248_0
+  - python=3.9.18=h0755675_0_cpython
+  - pytorch-cpu=1.13.1=cpu_py39hdb170b5_1
+  - pytorch-lightning=2.1.2=pyhd8ed1ab_0
+  - scipy=1.11.4=py39h474f0d3_0
+  - tensorboard=2.15.1=pyhd8ed1ab_0
+  - tqdm=4.66.1=pyhd8ed1ab_0
+  - pip:
+      - gym==0.23.0