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Found 3 papers, 2 papers with code
Learning-based agricultural management in partially observable environments subject to climate variability
However, our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events.
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments
We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task.
Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
At first, the obtained optimal policy from PPO is compared to those from DQN and DDQN.
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