4 citations · 7 across the 3 of their papers we have counts for
8 papers
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search
Wei Tang, Yiheng Duan, Yaroslav Kharkov +3
Quantum computers have the potential to outperform classical computers in important tasks such as optimization and number factoring. They are characterized by limited connectivity,…
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning
Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola
This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity. First, we show that simp…
TraDE: Transformers for Density Estimation
Rasool Fakoor, Pratik Chaudhari, Jonas Mueller +1
We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data. Our model is trained using a penalized maximu…
Meta-Q-Learning
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto +1
This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learnin…
P3O: Policy-on Policy-off Policy Optimization
Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola
On-policy reinforcement learning (RL) algorithms have high sample complexity while off-policy algorithms are difficult to tune. Merging the two holds the promise to develop efficie…
Differentiable Greedy Networks
Thomas Powers, Rasool Fakoor, Siamak Shakeri +4
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is t…