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Combining Causal and Reinforcement Learning| title | Combining Causal and Reinforcement Learning |
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| start_date | 2023/07/17 |
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| schedule | 14h |
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| online | no |
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| location_info | Bât. IMAG salle séminaire 1 |
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| summary | Reinforcement Learning (RL) has been successfully applied to domains like games and robotics, however, it suffers from long training times. Incorporating models into RL (model-based ML) offers an alternative to produce faster convergence times.
Causal models (CM) have recently gain popularity due to their reasoning capabilities, however, learning causal models requires interventional data. RL naturally generates interventional data in its action variables.
In this talk, I will describe a framework to concurrently learn policies from RL and causal models, and how both learning processes can benefit from each other. In particular, on one hand, we propose to change the exploration strategy used in RL to favor the learning of CMs. On the other hand, CMs are used to guide/constrain the action selection strategy of RL to reduce its convergence times. Once a CM is learned for a particular domain, it can be easily transferred to another similar domain. We will present experimental results of this combination of causal and reinforcement learning, show its benefits, and how CMs can be transferred to another domain. |
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| responsibles | NC |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2023/07/18 15:45 UTC |
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