pathak22/noreward-rl
[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning
This project helps AI researchers train intelligent agents in environments where external rewards are scarce or completely absent. It takes an agent and an environment, and outputs an agent capable of exploring and learning tasks primarily driven by an internal sense of 'curiosity.' This is ideal for researchers working on advanced AI for games, robotics, or complex simulations.
1,471 stars. No commits in the last 6 months.
Use this if you are an AI researcher experimenting with reinforcement learning agents in challenging environments that provide very few or no explicit rewards.
Not ideal if you are looking for a plug-and-play solution for environments with clear and frequent reward signals, or if you are not an AI/ML researcher.
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Dec 07, 2022
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