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Amir Barati Farimani: Creative Robots with Deep Reinforcement Learning | CMU RI Seminar


curated by | April 6, 2019

Link to video on YouTube

“Recent advances in Deep Reinforcement Learning (DRL) algorithms provided us with the possibility of adding intelligence to robots. Recently, we have been applying a variety of DRL algorithms to the tasks that modern control theory may not be able to solve. We observed intriguing creativity from robots when they are constrained in reaching a certain goal. To introduce the topic, I will talk about some of the experiments that are being done to show the capabilities and limitations of modern Deep Reinforcement Learning approaches, including those of sparse rewards and continuous observations and action spaces. An in depth explanation of how Hindsight Experience Replay (HER) has been used to obtain dense results from sparse environments when using Deep Deterministic Policy Gradient (DDPG) agents will be given. I will then show how we have modified some of these experiments to have a deeper understanding of the intelligence we are developing, and what are the baseline environmental characteristics that make the robots achieve higher levels of creativity during their problem solving scenarios.”

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