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User-driven narrative variation in large story domains using monte carlo tree search.

, , and . AAMAS, page 69-76. IFAAMAS/ACM, (2014)

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Providing Uncertainty-Based Advice for Deep Reinforcement Learning Agents (Student Abstract)., , , and . AAAI, page 13913-13914. AAAI Press, (2020)On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman., , , and . AIIDE, page 24-30. AAAI Press, (2019)Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments., , , , , and . ISS, page 339-342. ACM, (2019)Is multiagent deep reinforcement learning the answer or the question? A brief survey., , and . CoRR, (2018)A survey and critique of multiagent deep reinforcement learning., , and . Auton. Agents Multi Agent Syst., 33 (6): 750-797 (2019)Stochastic Planning in Large Search Spaces.. IJCAI, page 4000-4001. IJCAI/AAAI Press, (2016)Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning., , and . AIIDE, page 38-44. AAAI Press, (2019)User-driven narrative variation in large story domains using monte carlo tree search., , and . AAMAS, page 69-76. IFAAMAS/ACM, (2014)Stochastic Tree Search with Useful Cycles for patrolling problems., , , and . ICRA, page 1289-1294. IEEE, (2015)Action Guidance with MCTS for Deep Reinforcement Learning., , and . AIIDE, page 153-159. AAAI Press, (2019)