Search Results for author:
Found 7 papers, 4 papers with code
Intelligent Agricultural Management Considering N$_2$O Emission and Climate Variability with Uncertainties
This study examines how artificial intelligence (AI), especially Reinforcement Learning (RL), can be used in farming to boost crop yields, fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil.
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.
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic
This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces.
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
This paper studies the control synthesis of motion planning subject to uncertainties.
Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction
This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of linear temporal logic (LTL) specifications.
