Deep variational reinforcement learning for POMDPs
Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this...
Main Authors: | Igl, M, Zintgraf, L, Le, T, Wood, F, Whiteson, S |
---|---|
Format: | Conference item |
Udgivet: |
Journal of Machine Learning Research
2018
|
Lignende værker
-
Exploration in approximate hyper-state space for meta reinforcement learning
af: Zintgraf, L, et al.
Udgivet: (2021) -
Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs
af: Pineau, Joelle, et al.
Udgivet: (2017) -
VariBAD: a very good method for Bayes-adaptive deep RL via meta-learning
af: Zintgraf, L, et al.
Udgivet: (2020) -
Multi-Agent Active Perception Based on Reinforcement Learning and POMDP
af: Tarik Selimovic, et al.
Udgivet: (2024-01-01) -
TreeQN and ATreeC: differentiable tree planning for deep reinforcement learning
af: Farquhar, G, et al.
Udgivet: (2018)