A General Markov Decision Process Framework For Directly Learning Optimal Control Policies

Abstract

We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities. Derivations of general classes of our control-based RL methods are presented, together with forms of exploration and exploitation in learning and applying the optimal control policy over time. Our general MDP framework extends the classical Bellman operator and optimality criteria by generalizing the definition and scope of a policy for any given state. We establish the convergence and optimality-both in general and within various control paradigms (e.g., piecewise linear control policies)-of our control-based methods through this general MDP framework, including convergence of \(Q\)-learning within the context of our MDP framework. Our empirical results demonstrate and quantify the significant benefits of our approach.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylu2019a

Related papers

• On Learning History Based Policies For Controlling Markov Decision Processes (2022)0.00
• A General Control-theoretic Approach For Reinforcement Learning: Theory And Algorithms (2024)0.00
• Direct And Indirect Reinforcement Learning (2019)10.74
• Multi-timescale Ensemble Q-learning For Markov Decision Process Policy Optimization (2024)6.34
• Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach (2023)0.00
• From Reinforcement Learning To Optimal Control: A Unified Framework For Sequential Decisions (2019)0.00
• Towards An Adaptable And Generalizable Optimization Engine In Decision And Control: A Meta Reinforcement Learning Approach (2024)0.00
• Learning And Planning In Average-reward Markov Decision Processes (2020)0.00