Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learning
📰 ArXiv cs.AI
Lexpop framework uses deep reinforcement learning to solve partially observable Markov decision processes (POMDPs) with finite-state controllers
Action Steps
- Employ deep reinforcement learning to train a neural network
- Use the neural network to compute policies for POMDPs
- Implement finite-state controllers to execute the computed policies
- Evaluate the performance of the controllers across multiple POMDPs
Who Needs to Know This
AI engineers and researchers working on POMDPs and deep reinforcement learning can benefit from this framework to improve scalability and robustness of their solutions
Key Insight
💡 Deep reinforcement learning can be used to train finite-state controllers for POMDPs, improving scalability and robustness
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💡 Lexpop framework solves POMDPs using deep reinforcement learning!
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