q-Learning in Continuous TimeYanwei JiaXun Yu ZhouJournal of Machine Learning Research
We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang et al. (2020). As the conventional (big) Q-function collapses in continuous time, we consider its first-order approximatio...
Reinforcement learning in continuous time: advantage updating learning systemslinear quadratic controlneural netsQ-learningadvantage updatingcontinuous time systemconvergenceA new algorithm for reinforcement learning, advantage ... LCB Iii - IEEE World Congress on IEEE International Conference on Neural Networks...
This paper studies the continuous-time q-learning in the mean-field jump-diffusion models from the representative agent's perspective. To overcome the challenge when the population distribution may not be directly observable, we introduce the integrated q-function in decoupled form (decoupled Iq-funct...
Importantly, we assume that the agent cannot observe the population’s distribution and needs to estimate it in a model-free manner. The asymptotic MFG and MFC problems are also presented in continuous time and space, and compared with classical (non-asymptotic or stationary) MFG and MFC ...
算法1. Continuous Q-Learning with NAF(此处为代码) Randomly initialize normalized Q network Q(x,u|θQ). 0 Q0 Q Initialize target network Q with weight θ←θ . Initialize replay buffer R ← ∅. for episode=1,M do Initialize a random process N for action exploration Receive initial obse...
Q-Learning for Continuous-Time Linear Systems: A Data-Driven Implementation of the Kleinman Algorithm Q-learningA data-driven strategy to estimate the optimal feedback and the value function in an infinite-horizon, continuous-time, linear-quadratic optimal ... C Possieri,M Sassano - 《IEEE Tra...
Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the ...
A Q-learning solution to the discrete-time linear quadratic zero-sum game was first developed in Al-Tamimi, Lewis, and Abu-Khalaf (2007), where its application to the H-infinity control problem was shown. Later, the continuous-time zero-sum game problem was solved using partially model-free...
The authors proposed that agents can effectively use their own exploration behavior by identifying the possible goals in the environment to find effective strategies in the case of unknown goals. Vamvoudakis et al. [12] proposed the use of Q-learning technique for continuous-time-based graphical ...