<commit_before># OpenAI Gym experiments

My implementations of [normalized advantage functions](http://arxiv.org/abs/1603.00748) (NAF) for continuous actions spaces and [dueling network architecture](http://arxiv.org/abs/1511.06581) (DUEL) for discrete action spaces. 

Example results with NAF:
 * [InvertedPendulum-v1](https://gym.openai.com/evaluations/eval_CzoNQdPSAm0J3ikTBSTCg)
 * [Pendulum-v0](https://gym.openai.com/evaluations/eval_gyzqV97QESg0M6WrUsiIQ)
 
Example results with DUEL:
 * [CartPole-v0](https://gym.openai.com/evaluations/eval_sOUmkzSy26GIWJ5IIQeA)
 * [MountainCar-v0](https://gym.openai.com/evaluations/eval_nAU6XkQhSuKrVNNZdQ5xQ)
 * [Acrobot-v0](https://gym.openai.com/evaluations/eval_TkAOrmYAQ9eoiTujoCgw)

## Prerequisites

You will need:
 * Python 2.7
 * [OpenAI Gym](https://gym.openai.com/)
 * [Keras](http://keras.io/)
 * [Numpy](http://www.numpy.org/)
 * [Scikit-Learn](http://scikit-learn.org) (if using imagination rollouts)<commit_msg>Updated Pendulum-v0 link.<commit_after># OpenAI Gym experiments

My implementations of [normalized advantage functions](http://arxiv.org/abs/1603.00748) (NAF) for continuous actions spaces and [dueling network architecture](http://arxiv.org/abs/1511.06581) (DUEL) for discrete action spaces. 

Example results with NAF:
 * [InvertedPendulum-v1](https://gym.openai.com/evaluations/eval_CzoNQdPSAm0J3ikTBSTCg)
 * [Pendulum-v0](https://gym.openai.com/evaluations/eval_IU3wehAQQRuJRbzMjy26QQ)
 
Example results with DUEL:
 * [CartPole-v0](https://gym.openai.com/evaluations/eval_sOUmkzSy26GIWJ5IIQeA)
 * [MountainCar-v0](https://gym.openai.com/evaluations/eval_nAU6XkQhSuKrVNNZdQ5xQ)
 * [Acrobot-v0](https://gym.openai.com/evaluations/eval_TkAOrmYAQ9eoiTujoCgw)

## Prerequisites

You will need:
 * Python 2.7
 * [OpenAI Gym](https://gym.openai.com/)
 * [Keras](http://keras.io/)
 * [Numpy](http://www.numpy.org/)
 * [Scikit-Learn](http://scikit-learn.org) (if using imagination rollouts)
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