538 citations · 788 across the 7 of their papers we have counts for
3 papers · 1 filter
Learning to Walk via Deep Reinforcement Learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou +3
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domai…
Composable Deep Reinforcement Learning for Robotic Manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou +3
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, mod…
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel +1
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer f…