End-to-End Policy Gradient Method for POMDPs and Explainable Agents
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Abstract (arXiv)
Real-world decision-making problems are often partially observable, and many can be formulated as a Partially Observable Markov Decision Process (POMDP). When we apply reinforcement learning (RL) algorithms to the POMDP, reasonable estimation of the hidden states can help solve the problems. Furthermore, explainable decision-making is preferable, considering their application to real-world tasks such as autonomous driving cars. We proposed an RL algorithm that estimates the hidden states by end-to-end training, and visualize the estimation as a state-transition graph. Experimental results demonstrated that the proposed algorithm can solve simple POMDP problems and that the visualization makes the agent's behavior interpretable to humans.
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References · 8
- 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
- 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 231
- 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
- 2020Explainable Reinforcement Learning: A Surveycited by 141
- 2018Deep Variational Reinforcement Learning for POMDPscited by 125
- 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
- 2018Toward Interpretable Deep Reinforcement Learning with Linear Model U-Treescited by 65
- 2018On Improving Deep Reinforcement Learning for POMDPscited by 50
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