Open-ended Environments for Advancing RL

Jul 18, 2020

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The field of reinforcement learning is pushed forwards by the presence of challenging environments. Over the years, the complexity of these environments has continued to increase, but the question is how can we continue to push the complexity of environments with respect to the optimal policy complexity in a scalable manner. Here I will discuss using multi-agent environments to create more open-ended environments, and discuss examples of our work to move in this direction with Capture the Flag and Starcraft 2. Finally I will discuss some future directions for generating even more open-ended environments to further push our RL algorithms.

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The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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