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Alexandre Piche

2 accepted papers

2019

Probabilistic Planning with Sequential Monte Carlo methods

ICLR 2019poster

In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories. This enables us to use sampling methods, and thus, tackle planning in continuous domains using a fixed computational budget. We design a new algorithm, Se…

Cited by 58SourcePDFScholar
2018

Reward Estimation for Variance Reduction in Deep Reinforcement Learning

CoRL 2018

Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or co