What OpenAI Gym environments are your favourite for learning RL algorithms?

• Minigrid

  8 2,010 6.9 Python

  Simple and easily configurable grid world environments for reinforcement learning

  

For learning and experimentation with RL algorithms, I suggest using a grid world implementation: observations are simple enough (most implementations have a one-hot layered observation) that you do not need deep conv layers to learn complex visual features. You can also make grid worlds as simple or as complex as you like by adding enemies, objects, key-door pairs, changing the size of the grid or decreasing observation radius, etc. There is a reason they are commonly used in research.

• MinAtar

  2 267 3.8 Python

For Image state environment, I also recommend MinAtar, which use dense reward whereas gym-minigrid use sparse reward, so we could more concentrate on the algorithms, not exploration methods. Of course, gym-minigrid is elegant environment!!

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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