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 # openai_playground
-This collection of Python modules implements some Reinforcement Learning algorithms, most notably **Deep Q Networks (DQN)** and **Prioritized Experience Replay (PER)**, where the proportional prioritization variant has been implemented.. It has been built to solve [OpenAI Gym environments]((https://gym.openai.com/), although it has only been tested on classic control environments with discrete action sets.
+This collection of Python modules implements some Reinforcement Learning algorithms, most notably **Deep Q Networks (DQN)** and **Prioritized Experience Replay (PER)**, where the proportional prioritization variant has been implemented.. It has been built to solve [OpenAI Gym environments](https://gym.openai.com/), although it has only been tested on classic control environments with discrete action sets.
 
 The code supports a variety of hyper parameters, that are usually tuned to particular environments. Bayesian optimization with [Scikit-Optimize](https://scikit-optimize.github.io/) is a simple way of tuning those hyper parameters.