Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning FP Such, V Madhavan, E Conti, J Lehman, KO Stanley, J Clune arXiv preprint arXiv:1712.06567, 2017 | 919 | 2017 |
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-seeking Agents E Conti, V Madhavan, FP Such, J Lehman, K Stanley, J Clune Advances in Neural Information Processing Systems (NeurIPS), 5027-5038, 2018 | 416 | 2018 |
Benchmarking Batch Deep Reinforcement Learning Algorithms S Fujimoto, E Conti, M Ghavamzadeh, J Pineau Neural Information Processing Systems (NeurIPS) 2019 Workshop, 2019 | 200 | 2019 |
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform J Gauci, E Conti, Y Liang, K Virochsiri, Y He, Z Kaden, V Narayanan, X Ye, ... International Conference on Machine Learning (ICML) 2019 Workshop, 2018 | 161 | 2018 |
Training neural networks using evolution based strategies and novelty search E Conti, V Madhavan, JM Clune, FP Such, JA Lehman, KO Stanley US Patent 11,068,787, 2021 | 2 | 2021 |
Scalable parameter encoding of artificial neural networks obtained via an evolutionary process FP Such, JM Clune, KO Stanley, E Conti, V Madhavan, JA Lehman US Patent 10,599,975, 2020 | 1 | 2020 |