Aditya Rawal
Aditya Rawal is a research scientist at Uber AI Labs. His interests lies at the convergence of two research fields - neuroevolution and deep learning. His belief is that evolutionary search can replace human ingenuity in creating next generation of deep networks. Previously, Aditya received his MS/PhD in Computer Science from University of Texas at Austin, advised by Prof. Risto Miikkulainen. During his PhD, he developed neuroevolution algorithms to evolve recurrent architectures for sequence-prediction problems and construct multi-agent systems that cooperate, compete and communicate.
Research Papers
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
T. Miconi, A. Rawal, J. Clune, K. Stanley
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains. To address this shortfall, we introduce a new algorithm called Go-Explore. [...] [PDF]
International Conference on Learning Representations (ICLR), 2019
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, which is the dominant method to improve performance on hard-exploration domains. To address this shortfall, we introduce a new algorithm called Go-Explore. [...] [PDF]
International Conference on Learning Representations (ICLR), 2019