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Found 3 papers, 1 papers with code
Dropout's Dream Land: Generalization from Learned Simulators to Reality
By training the World Model using dropout, the dream environment is capable of creating a nearly infinite number of different dream environments.
Policy Tree Network
However, decision-time planning with implicit dynamics models in continuous action space has proven to be a difficult problem.
Model-based Reinforcement Learning
Policy Gradient Methods
+3
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space
This paper proposes a novel deep reinforcement learning architecture that was inspired by previous tree structured architectures which were only useable in discrete action spaces.
