An Intuition-Based Approach to Reinforcement Learning


Reinforcement learning (RL) has achieved remarkable success in various tasks, such as defeating all-human teams in MMP (massive multi-player) games, advances in robotics, and astonishing results in the protein folding problem in chemistry. Expertise in RL requires strong knowledge of machine learning, statistics, and areas of mathematics. Moreover, RL contains many concepts that seem "fuzzy" and hence can be challenging for beginners who are trying to learn RL. However, this session provides the intuition of various RL concepts, such as exploit/explore and maximization of expected reward, along with real-life examples of these concepts. Attendees will also see a comparison of greedy versus epsilon greedy, and why epsilon greedy can solve tasks that cannot be solved using a greedy approach. Some of the preceding concepts will be illustrated during the presentation of the n-chain task in RL, whose solution clearly requires an epsilon greedy algorithm. The target audience for this session is for beginners who have no experience with RL.


Oswald is a former PhD Candidate (ABD) in Mathematics, an education fanatic (5 degrees), and an author of 40 technical books. He has worked for Oracle, AAA, and Just Systems of Japan as well as various startups. He has lived/worked in 5 countries on three continents, and in a previous career he worked in South America, Italy, and the French Riviera, and has traveled to 70 countries on five continents. He has worked from C/C++/Java developer to CTO, comfortable in 4 languages, and currently he is an AI (ML,DL,NLP,DRL) adjunct instructor at UCSC and works on NLP-related tasks in a start-up in the Bay Area.

Open Data Science




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