4 citations · 9 across the 4 of their papers we have counts for
4 papers
Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du +7
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planni…
Parallel -Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
Zechu Li, Tao Chen, Zhang-Wei Hong +2
Reinforcement learning is time-consuming for complex tasks due to the need for large amounts of training data. Recent advances in GPU-based simulation, such as Isaac Gym, have sped…
Offline RL Policies Should be Trained to be Adaptive
Dibya Ghosh, Anurag Ajay, Pulkit Agrawal +1
Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown. The most common way to approach this challenge…
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…