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Creators/Authors contains: "Milani, Stephanie"

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  1. ; ; ; (, AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; (, ACM)
    Full Text Available 
  3. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    null (Ed.)
    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. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    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. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks. 
    more » « less
    Full Text Available 
  6. ; ; ; ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    null (Ed.)
    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. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks. 
    more » « less
    Accepted Manuscript Full Text Available