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ML Conference 2019 - Human / AI Interaction Loop Training as a new Approach for interactive Learning
Discover human-AI interaction loop training, a new approach for interactive learning that combines imitation, reinforcement, and hybrid learning to improve speed and accuracy.
- Human-AI interaction loop training is a new approach for interactive learning, enabling agents to learn from humans and humans to learn from agents.
- The approach combines imitation learning, reinforcement learning, and hybrid learning to improve the speed and accuracy of learning.
- Cogment is an open-source framework for building interactive learning systems that integrates human and AI agents.
- The framework uses a Cogment.yml file to define the environment, agents, and rewards, and allows for the creation of custom environments and agents.
- Imitation learning can be used to train agents to mimic human behavior, but may not be effective in all situations.
- Reinforcement learning can be used to train agents to maximize rewards, but may require a large amount of data and computational resources.
- Hybrid learning combines the strengths of imitation and reinforcement learning to improve the speed and accuracy of learning.
- The credit assignment problem is a challenge in reinforcement learning, where the agent must determine which actions led to rewards or penalties.
- The human-AI interaction loop training approach can help to improve the alignment between human and AI goals, and can be used in a variety of applications, including gaming, robotics, and healthcare.