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. Techniques

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... such as domain randomization and induced noise during training were utilized to bring variability and ultimately improve the learning policies. The experimental results demonstrate the effectiveness of the Actor-Critic reinforcement learning technique to bridge the reality gap.