| Action branching architectures for deep reinforcement learning A Tavakoli, F Pardo, P Kormushev AAAI Conference on Artificial Intelligence (AAAI), 4131-4138, 2018 | 302 | 2018 |
| Time limits in reinforcement learning F Pardo, A Tavakoli, V Levdik, P Kormushev International Conference on Machine Learning (ICML), 4045-4054, 2018 | 171 | 2018 |
| On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks M Seitzer, A Tavakoli, D Antic, G Martius International Conference on Learning Representations (ICLR), 2022 | 81 | 2022 |
| A neural network oracle for quantum nonlocality problems in networks T Kriváchy, Y Cai, D Cavalcanti, A Tavakoli, N Gisin, N Brunner npj Quantum Information 6 (1), 70, 2020 | 46 | 2020 |
| Using a logarithmic mapping to enable lower discount factors in reinforcement learning H van Seijen, M Fatemi, A Tavakoli Neural Information Processing Systems (NeurIPS), 14134-14144, 2019 | 33 | 2019 |
| Learning to represent action values as a hypergraph on the action vertices A Tavakoli, M Fatemi, P Kormushev International Conference on Learning Representations (ICLR), 2021 | 21 | 2021 |
| Exploring restart distributions A Tavakoli, V Levdik, R Islam, CM Smith, P Kormushev Multidisciplinary Conference on Reinforcement Learning and Decision Making …, 2019 | 15* | 2019 |
| Crowdsourced coordination through online games A Tavakoli, H Nalbandian, N Ayanian ACM/IEEE International Conference on Human-Robot Interaction (HRI), 527-528, 2016 | 9 | 2016 |
| Orchestrated value mapping for reinforcement learning M Fatemi, A Tavakoli International Conference on Learning Representations (ICLR), 2022 | 5 | 2022 |
| On Structural and Temporal Credit Assignment in Reinforcement Learning A Tavakoli Imperial College London, 2021 | 2 | 2021 |
| Constructive neural network models for studying Bell nonlocality and entanglement TM Krivachy, Y Cai, D Cavalcanti, A Tavakoli, N Gisin, N Brunner | | 2022 |
