356 citations · 2k across the 77 of their papers we have counts for
10 papers · 1 filter
Generalizing Skills with Semi-Supervised Reinforcement Learning
Chelsea Finn, Tianhe Yu, Justin Fu +2
Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the v…
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
Chelsea Finn, Paul Christiano, Pieter Abbeel +1
Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously l…
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap +1
Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic application…
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +2
Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their…
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
William Montgomery, Anurag Ajay, Chelsea Finn +2
Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without requiring extensive manual engineering. However, robotic skill l…
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran +1
Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich f…