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8 Best Alternatives To OpenAI Safety Gym

8 Best Alternatives To OpenAI Safety Gym

  • Safety Gym has use cases across the reinforcement learning ecosystem
8 Best Alternatives To OpenAI Safety Gym

Two years ago, Open AI released Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.

Safety Gym has use cases across the reinforcement learning ecosystem.

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The open-source release is available on GitHub, where researchers and developers can get started with just a few lines of code. 

In this article, we will explore some of the alternative environments, tools and libraries for researchers to train machine learning models. 

AI Safety Gridworlds

AI Safety Gridworlds is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. Developed by DeepMind, these environments are implemented in PyColab, a highly customisable gridworld game engine. The suite includes the following environments: Safe interruptibility; avoid side effects; absent supervisor; reward gaming; self-modification; distributional shift; robustness to adversaries; and safe exploration. 

For more details on AI Safety Gridworlds, check out the research paper and the GitHub repository.  

DeepMind Control Suite 

DeepMind’s DM Control Suite consists of physics-based simulations for reinforcement learning agents, using MuJoCo. The reinforcement learning environment consists of all the necessary components such as ‘standard structure’ for task control and ‘rewards’ agents can infer. 

The introductory tutorial for using this package is available on Colab Notebook. Also, check out the GitHub repository here.

DeepMind Lab 

DeepMind Lab is a 3D learning environment based on ID software Quake III Arena via ioquake3 and other open-source tools. It provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. The primary purpose of the tool is to act as a testbed for research in deep reinforcement learning. 

Check out the DeepMind Lab GitHub repository here

AWS DeepRacer

AWS DeepRacker is an autonomous 1/18th scale race car designed to test reinforcement learning models by racing on a physical track. It uses cameras to view the track and a reinforcement model to control throttle and steering. The car shows how a model trained in a simulated environment is transferred to the real world. 

AWS DeepRacer Evo car comes with the original AWS DeepRacer car, an additional 4-megapixel camera module that forms stereo vision with the original one, a scanning LiDAR, a shell that can fire both the stereo camera and LiDAR, and a few accessories and easy-to-use tools for a quick installation. 

For more details about AWS DeepRacer, check out its website


SafeML is one of the popular techniques for safety monitoring of machine learning classifiers. It addresses both safety and security within a single concept of protection applicable during the operation of ML systems, where it actively monitors the behaviour and operational context of the data-driven system based on distance measures of the Empirical Cumulative Distribution Function (ECDF). 

The idea of SafeML was proposed by ‪Koorosh Aslansefat et al. to monitor the classifiers’ decisions when there is no available label. 

Check out more examples and use cases with SafeML here

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Tensor Trade is an open-source reinforcement learning framework for training, evaluating, and deploying robust trading algorithms using reinforcement learning. It is used for building complex investment strategies that are run across HPC machine distribution. 

TensorTrade leverages existing tools and pipelines provided by NumPy, Pandas, Gym, Keras and Tensorflow to enable fast experimentation with algorithmic trading strategies.

The open-source code is available on GitHub


Developed by Facebook, ReAgent is an open-source end-to-end platform for applied reinforcement learning. ReAgent uses PyTorch for modeling and training and TorchScript for model serving. It contains workflows to train popular deep reinforcement learning algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation and optimised serving. 

Check out ReAgent’s GitHub repository here.


Clever Hans provides reference implementation of attacks against machine learning models to help with benchmarking models against adversarial examples. The open-source library is maintained by the CleverHans Lab at the University of Toronto. Its latest version supports three frameworks, including JAX, PyTorch and TensorFlow. 

More details about CleverHans is found in GitHub

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