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Understanding the impact of entropy on policy optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi +1
Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with \emph{exploration} by encouraging the selection of mor…
Kernel Exponential Family Estimation via Doubly Dual Embedding
Bo Dai, Hanjun Dai, Arthur Gretton +3
We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space. Key to our ap…
Smoothed Action Value Functions for Learning Gaussian Policies
Ofir Nachum, Mohammad Norouzi, George Tucker +1
State-action value functions (i.e., Q-values) are ubiquitous in reinforcement learning (RL), giving rise to popular algorithms such as SARSA and Q-learning. We propose a new notion…