diff --git a/README.md b/README.md
index eda2fbf..2127321 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,7 @@ This project implements deep reinforcement learning algorithms including followi
- Deep Reinforcement Learning with Double Q-learning
- Asynchronous Methods for Deep Reinforcement Learning
- Prioritized Experience Replay
+ - Continuous control with deep reinforcement learning
@@ -28,12 +29,20 @@ In my PC (i7 CPU, Titan-X Maxwell),
- Double-Q took 112 hours for 80M steps (shown 11M steps, nature network)
- Prioritized took 112 hours for 80M steps (shown 11M steps, nature network)
+
+## Torcs
+[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/RfAJCkJ8d2s/0.jpg)](https://www.youtube.com/watch?v=RfAJCkJ8d2s)
+
+After 1 hour training in simulator Torcs, it learns how to accelerate and turn the steering wheel.
+
+
## Requirements
- Python-2.7
- Numpy
- - Arcade-Learning-Environment
- Tensorflow-0.11
- opencv2
+ - Arcade-Learning-Environment
+ - Torcs (optional)
- Vizdoom (in working)
See this for installation.
@@ -45,6 +54,7 @@ Double DQN : python deep_rl_train.py /path/to/rom --drl double_dqn
Prioritized : python deep_rl_train.py /path/to/rom --drl prioritized_rank
A3C FF : python deep_rl_train.py /path/to/rom --drl a3c --thread-no 8
A3C LSTM : python deep_rl_train.py /path/to/rom --drl a3c_lstm --thread-no 8
+DDPG : python deep_rl_train.py torcs --ddpg
```
## How to retrain
@@ -73,3 +83,4 @@ While training you can send several debug commands in the console.
- https://github.com/tambetm/simple_dqn
- https://github.com/miyosuda/async_deep_reinforce
- https://github.com/muupan/async-rl
+ - https://github.com/yanpanlau/DDPG-Keras-Torcs