Search Results for author:
Found 2 papers, 0 papers with code
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty
We introduce Probabilistic Actor-Critic (PAC), a novel reinforcement learning algorithm with improved continuous control performance thanks to its ability to mitigate the exploration-exploitation trade-off.
PAC-Bayesian Soft Actor-Critic Learning
Actor-critic algorithms address the dual goals of reinforcement learning (RL), policy evaluation and improvement, via two separate function approximators.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
