Research Post
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlike existing upper-confidence-bound (UCB) based approaches, which are often computationally intractable, our algorithm drives exploration by simply perturbing the training data with judiciously chosen i.i.d. scalar noises. To attain optimistic value function estimation without resorting to a UCB-style bonus, we introduce an optimistic reward sampling procedure. When the value functions can be represented by a function class F, our algorithm achieves a worst-case regret bound of O˜(poly(dEH)√T) where T is the time elapsed, H is the planning horizon and dE is the eluder dimension of F. In the linear setting, our algorithm reduces to LSVI-PHE, a variant of RLSVI, that enjoys an O˜(d3H3√T) regret. We complement the theory with an empirical evaluation across known difficult exploration tasks.
Jan 31st 2023
Research Post
Jan 20th 2023
Research Post
Aug 8th 2022
Research Post
Read this research paper co-authored by Canada CIFAR AI Chair Angel Chang: Learning Expected Emphatic Traces for Deep RL
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