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Found 3 papers, 1 papers with code
Sample Efficient Robot Learning with Structured World Models
Reinforcement learning has been demonstrated as a flexible and effective approach for learning a range of continuous control tasks, such as those used by robots to manipulate objects in their environment.
Agent-aware State Estimation in Autonomous Vehicles
Autonomous systems often operate in environments where the behavior of multiple agents is coordinated by a shared global state.
Planning with Abstract Learned Models While Learning Transferable Subtasks
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
