Karl Sims,et al. Evolving 3d morphology and behavior by competition , 1994 .
 Richard K. Belew,et al. Methods for Competitive Co-Evolution: Finding Opponents Worth Beating , 1995, ICGA.
 G. Hunt. Manufacture and use of hook-tools by New Caledonian crows , 1996, Nature.
 Charles Ofria,et al. Avida: A Software Platform for Research in Computational Evolutionary Biology , 2004, Artificial Life.
 Risto Miikkulainen,et al. Competitive Coevolution through Evolutionary Complexification , 2011, J. Artif. Intell. Res..
 Michael L. Littman,et al. An analysis of model-based Interval Estimation for Markov Decision Processes , 2008, J. Comput. Syst. Sci..
 Andre Cohen,et al. An object-oriented representation for efficient reinforcement learning , 2008, ICML '08.
 Richard L. Lewis,et al. Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective , 2010, IEEE Transactions on Autonomous Mental Development.
 Sergey Levine,et al. Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models , 2015, ArXiv.
 Tim Taylor. Requirements for Open-Ended Evolution in Natural and Artificial Systems , 2015, ArXiv.
 Shakir Mohamed,et al. Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning , 2015, NIPS.
 Shimon Whiteson,et al. Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.
 Filip De Turck,et al. VIME: Variational Information Maximizing Exploration , 2016, NIPS.
 Rob Fergus,et al. Learning Multiagent Communication with Backpropagation , 2016, NIPS.
 Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
 Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
 Sergey Levine,et al. High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.
 Filip De Turck,et al. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning , 2016, NIPS.
 Marc G. Bellemare,et al. Count-Based Exploration with Neural Density Models , 2017, ICML.
 Pierre-Yves Oudeyer,et al. Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning , 2017, ArXiv.
 Alexei A. Efros,et al. Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
 Demis Hassabis,et al. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm , 2017, ArXiv.
 Yi Wu,et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.
 Pieter Abbeel,et al. Reverse Curriculum Generation for Reinforcement Learning , 2017, CoRL.
 Yuval Tassa,et al. Emergence of Locomotion Behaviours in Rich Environments , 2017, ArXiv.
 Joel Z. Leibo,et al. Multi-agent Reinforcement Learning in Sequential Social Dilemmas , 2017, AAMAS.
 S. Shankar Sastry,et al. Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning , 2017, ArXiv.
 Joel Z. Leibo,et al. A multi-agent reinforcement learning model of common-pool resource appropriation , 2017, NIPS.
 Daniel L. K. Yamins,et al. Learning to Play with Intrinsically-Motivated Self-Aware Agents , 2018, NeurIPS.
 Pieter Abbeel,et al. Emergence of Grounded Compositional Language in Multi-Agent Populations , 2017, AAAI.
 Ilya Kostrikov,et al. Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play , 2017, ICLR.
 Risto Miikkulainen,et al. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities , 2018, Artificial Life.
 Marcin Andrychowicz,et al. Asymmetric Actor Critic for Image-Based Robot Learning , 2017, Robotics: Science and Systems.
 Sergey Levine,et al. Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations , 2017, Robotics: Science and Systems.
 Joshua B. Tenenbaum,et al. The Tools Challenge: Rapid Trial-and-Error Learning in Physical Problem Solving , 2019, CogSci.
 Nando de Freitas,et al. Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning , 2019, ICML.
 Guy Lever,et al. Human-level performance in 3D multiplayer games with population-based reinforcement learning , 2018, Science.
 Sergey Levine,et al. Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight , 2019, Robotics: Science and Systems.
 Kevin A. Smith,et al. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning , 2019, Proceedings of the National Academy of Sciences.