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Improving Reinforcement Learning with Confidence-Based Demonstrations.

, and . IJCAI, page 3027-3033. ijcai.org, (2017)

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Protecting against evaluation overfitting in empirical reinforcement learning., , , and . ADPRL, page 120-127. IEEE, (2011)When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty., , , , and . AAMAS, page 109-116. IFAAMAS, (2010)Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL., , and . CoRR, (2018)Multi-objectivization of reinforcement learning problems by reward shaping., , , , , and . IJCNN, page 2315-2322. IEEE, (2014)Speeding up Tabular Reinforcement Learning Using State-Action Similarities., , and . AAMAS, page 1722-1724. ACM, (2017)An Exploration Strategy Facing Non-Stationary Agents., , , , and . AAMAS, page 922-923. ACM, (2017)Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning., and . CoRR, (2017)Lifelong learning for disturbance rejection on mobile robots., , , , , , and . IROS, page 3993-3998. IEEE, (2016)An exploration strategy for non-stationary opponents., , , , and . Auton. Agents Multi Agent Syst., 31 (5): 971-1002 (2017)A survey and critique of multiagent deep reinforcement learning., , and . Auton. Agents Multi Agent Syst., 33 (6): 750-797 (2019)