- Published on August 23, 2021
- In AI Features
Inside Maze, A New Framework for Applied Reinforcement Learning
Maze supports scalable SOTA algorithms capable of handling multi-agent and hierarchical RL settings with dictionary action and observation spaces
- By Amit Naik
Last month, enliteAI released Maze, a new framework for applied reinforcement learning (RL). enliteAI is a technology provider for artificial intelligence specialised in reinforcement learning and computer vision.
(The source code of its latest framework is available on GitHub. Also, check out enliteAI's 'GettingStarted' notebooks to start experimenting with Maze.)
According to enliteAI, Maze supports scalable state-of-the-art (SOTA) algorithms capable of handling multi-agent and hierarchical RL settings with dictionary action and observation spaces. It also helps build powerful perception learners with building blocks for graph-convolution, self-and graph attention, recurrent architectures, action and observation masking, and more.
Plus, it offers support for complex
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Amit Naik
Amit Raja Naik is Senior Editorial Producer – Live Shows at AIM Network, driving India’s most influential AI and technology conversations. He leads content, narrative design, and visual storytelling, engaging with leaders, innovators, and policymakers to advance how technology impacts businesses, governance, and society.
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