A robot learns a stochastic environment model and uses imaginary rollouts to train a DQN, achieving faster learning on a gesture-based puzzle task than a baseline DQN.
Sample- efficient reinforcement learning with stochastic ensemble value expan- sion,
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Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
A robot learns a stochastic environment model and uses imaginary rollouts to train a DQN, achieving faster learning on a gesture-based puzzle task than a baseline DQN.