8 best alternatives to OpenAI Safety Gym
Two years ago, Open AI released Safety Gym, a suite of environments and tools for measuring progress toward reinforcement learning agents that meet safety constraints during training.
Safety Gym offers use cases in the reinforcement learning ecosystem.
The open source version is available on GitHub, where researchers and developers can get started with just a few lines of code.
In this article, we’ll explore some of the alternative environments, tools, and libraries for researchers to train machine learning models.
IA 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 customizable gridworld game engine. The suite includes the following environments: Safe interruptibility; avoid side effects; supervisor absent; reward games; self-modification; change of distribution; robustness to adversaries; and safe exploration.
For more details on AI Safety Gridworlds, see 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 the “standard structure” for task control and the “rewards” that agents can deduce.
The introductory tutorial on how to use this package is available on Colab Notebook. Also check out the GitHub repository here.
DeepMind Lab is a 3D learning environment based on Quake III Arena identification software via ioquake3 and other open source tools. It provides a suite of challenging 3D navigation and puzzle solving tasks for learning agents. The main purpose of the tool is to serve as a test bed for research on deep reinforcement learning.
Check out the DeepMind Lab GitHub repository here.
AWS DeepRacker is a 1 / 18th scale autonomous racing car designed to test reinforcement learning models while running on a physical track. It uses cameras to visualize the track and a reinforcement model to control the throttle and steering. The car shows how a model trained in a simulated environment is transferred to the real world.
The 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 trigger both the camera stereo and LiDAR, and some accessories and easy-to-use tools for quick setup.
For more details on AWS DeepRacer, see its website.
SafeML is one of the popular machine learning classifier security monitoring techniques. It addresses both safety and security within a single concept of protection applicable during the operation of ML systems, where it actively monitors the behavior and operational context of the data-driven system based on distance measurements. of the empirical cumulative distribution function (ECDF).
The idea of SafeML was proposed by Koorosh Aslansefat et al. to monitor classifier decisions when there is no label available.
Discover more examples and use cases with SafeML here.
Tensor Trade is an open source reinforcement learning framework for training, evaluating, and deploying robust trading algorithms using reinforcement learning. It is used to create complex investment strategies which are executed in the distribution of HPC machines.
TensorTrade leverages existing tools and pipelines provided by NumPy, Pandas, Gym, Keras and Tensorflow to enable rapid experimentation with algorithmic trading strategies.
The open source code is available on GitHub.
Developed by Facebook, ReAgent is an end-to-end open source platform for applied reinforcement learning. ReAgent uses PyTorch for modeling and training and TorchScript for serving models. It contains workflows for training popular deep reinforcement learning algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy assessment, and optimized delivery.
Check out ReAgent’s GitHub repository here.
Clever Hans provides a benchmark implementation of attacks on machine learning models to help compare models to conflicting 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 can be found in GitHub.
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