Zhaohan Daniel Guo

RESEARCH INTERESTS
● Sample efficient online reinforcement learning algorithms, with Probably Approximately
Correct bounds and/or regret bounds
● Smart exploration via optimism under uncertainty and Bayesian approaches
● Sample efficient off-policy algorithms via importance sampling and approximate models
with better bias-variance trade-offs
● More sample efficient deep reinforcement learning