DDPG

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Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

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# DDPG

Reimplementing DDPG from Continuous Control with Deep Reinforcement Learning based on OpenAI Gym and Tensorflow

[http://arxiv.org/abs/1509.02971](http://arxiv.org/abs/1509.02971)

It is still a problem to implement Batch Normalization on the critic network. However the actor network works well with Batch Normalization.

Some Mujoco environments are still unsolved on OpenAI Gym.

## Some Evaluations

1 [InvertedPendulum](https://gym.openai.com/evaluations/eval_mviLO6dZTCmtF1KSmprM1w#reproducibility)

2 [InvertedDoublePendulum](https://gym.openai.com/evaluations/eval_PtYUMaEUSwqS3YUYA6MOQ#reproducibility)

3 [Hopper unsolved](https://gym.openai.com/evaluations/eval_MwvKWh5CSp6SO8IAWU4pqw#reproducibility)

## How to use

```
git clone https://github.com/songrotek/DDPG.git
cd DDPG
python gym_ddpg.py

```
If you want to change the Gym environment, change ENV_NAME in gym_ddpg.py.

If you want to change the Network type, change import in ddpg.py such as

```
from actor_network_bn import ActorNetwork
to
from actor_network import ActorNetwork
```

## Reference
1 [https://github.com/rllab/rllab](https://github.com/rllab/rllab)

2 [https://github.com/MOCR/DDPG](https://github.com/MOCR/DDPG)

3 [https://github.com/SimonRamstedt/ddpg](https://github.com/SimonRamstedt/ddpg)