future_thoughts.md

Future Thoughts

• Neuroevolution of Self-Interpretable Agents - paper: https://arxiv.org/abs/2003.08165
• Neural Logic - paper: https://arxiv.org/pdf/1904.10729.pdf
• Neural Episodic Control - paper: https://arxiv.org/pdf/1703.01988.pdf
• Agent57 - release: https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark
• Distributional RL - overview: https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI
• Imagination-Augmented Agents - paper: https://arxiv.org/pdf/1707.06203.pdf code: https://github.com/clvrai/i2a-tf release: https://deepmind.com/blog/article/agents-imagine-and-plan
• Generative Adversarial Imagination - paper: https://arxiv.org/pdf/1904.13255v2.pdf
• World Models - paper: https://github.com/clvrai/i2a-tf code: https://github.com/hardmaru/WorldModelsExperiments
• Neuroevolution - https://towardsdatascience.com/deep-neuroevolution-genetic-algorithms-are-a-competitive-alternative-for-training-deep-neural-822bfe3291f5
  • https://towardsdatascience.com/reinforcement-learning-without-gradients-evolving-agents-using-genetic-algorithms-8685817d84f
  • https://eng.uber.com/deep-neuroevolution/
  • https://www.oreilly.com/radar/neuroevolution-a-different-kind-of-deep-learning/
• Population Based Policy Gradient - https://designrl.github.io/ https://papers.nips.cc/paper/7785-evolved-policy-gradients.pdf
• NEAT & HyperNEAT - http://blog.otoro.net/2016/05/07/backprop-neat/
• Compositional Pattern-Producing Networks - https://towardsdatascience.com/understanding-compositional-pattern-producing-networks-810f6bef1b88
• Multi-agent - https://arxiv.org/abs/1911.10635
• Novelty Search - https://eplex.cs.ucf.edu/papers/lehman_ecj11.pdf
• POET - https://eng.uber.com/poet-open-ended-deep-learning/
• Quality Diversity - https://www.frontiersin.org/articles/10.3389/frobt.2016.00040/full
• Minimal Criterion Coevolution - http://eplex.cs.ucf.edu/papers/brant_gecco17.pdf
• Environment / Ciriculum generation- https://dl.acm.org/doi/abs/10.1145/3205455.3205517
  • Procedural Content Generation - https://arxiv.org/abs/1911.13071
  • Progressive PCG - https://arxiv.org/abs/1806.10729
  • PCG via ML https://arxiv.org/abs/1702.00539
• Neuromodulation - http://www.evolvingai.org/miconi-t-rawal-clune-stanley-2019-backpropamine-training-self
• Coevolutionary Temporal Difference Learning - http://www.cs.put.poznan.pl/mszubert/pub/mscthesis.pdf
• Automatic Goal Generation - https://arxiv.org/pdf/1705.06366.pdf
• Hierarchical Reinforcement Learning - https://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning/
• Graph Neural Networks - https://arxiv.org/abs/1810.09202
• Dynamic HER - https://openreview.net/pdf?id=Byf5-30qFX
• Energy Based HER - https://arxiv.org/pdf/1810.01363.pdf
• Wieght Agnostic Neural Networks - https://arxiv.org/abs/1906.04358
• Improving Evolution Strategies with Generative Neural Networks - https://arxiv.org/pdf/1901.11271.pdf
• ANML Neuromodulation - https://arxiv.org/abs/2002.09571
• CMA-ES - https://en.wikipedia.org/wiki/CMA-ES
• NerveNet - https://openreview.net/pdf?id=S1sqHMZCb