CarsonScott/Dual-Process-Reinforcement
An intelligent agent that adaptively changes its thought processes to maximize cumulative reward
This project helps build intelligent agents that can adapt their decision-making strategy to maximize rewards. It takes in information about an environment and available actions, and outputs the optimal sequence of actions for the agent. This is for researchers or engineers developing sophisticated AI agents for dynamic and complex environments.
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Use this if you need an agent to efficiently learn and adapt its decision-making, balancing fast, intuitive responses with deliberate, problem-solving approaches.
Not ideal if your environment is simple and static, or if you require an agent with a purely reactive, single-strategy learning approach.
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Feb 19, 2017
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