2k citations · 3.4k across the 66 of their papers we have counts for
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Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Ben Eysenbach +3
In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast reinforcement learning (RL) strategies that transfer to similar tasks. Howe…
Learning to Reach Goals via Iterated Supervised Learning
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy +4
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitat…
A Multi-Task Gradient Descent Method for Multi-Label Learning
Lu Bai, Yew-Soon Ong, Tiantian He +1
Multi-label learning studies the problem where an instance is associated with a set of labels. By treating single-label learning problem as one task, the multi-label learning probl…
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
Xinghua Qu, Zhu Sun, Yew-Soon Ong +2
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing ever…
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Abhishek Gupta, Vikash Kumar, Corey Lynch +2
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable,…
Reciprocal Collision Avoidance for General Nonlinear Agents using Reinforcement Learning
Hao Li, Bowen Weng, Abhishek Gupta +2
Finding feasible and collision-free paths for multiple nonlinear agents is challenging in the decentralized scenarios due to limited available information of other agents and compl…