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Proceedings of the Canadian Conference on Artificial Intelligence
This paper investigates techniques that can be utilized to bridge the reality gap between virtual and physical robots, by implementing a virtual environment and a physical robotic platform to evaluate the robustness of transfer learning from virtual to real-world robots. The proposed approach utilizes two reinforcement (RL) learning methods: deep Q-learning and Actor-Critic methodology to create a model that can learn from a virtual environment and performs in a physical environment. Techniquesdoi:10.21428/594757db.09aa0c75 fatcat:z3ryo364mjgqhplmy7pkycka5a