3.7k citations · 6.5k across the 15 of their papers we have counts for
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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…
Latent Space Policies for Hierarchical Reinforcement Learning
Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel +1
We address the problem of learning hierarchical deep neural network policies for reinforcement learning. In contrast to methods that explicitly restrict or cripple lower layers of…
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…