Dynamic Regret of Online Markov Decision Processes.
Peng ZhaoLongfei LiZhi-Hua ZhouPublished in: ICML (2022)
Keyphrases
- markov decision processes
- online learning
- reinforcement learning
- finite state
- optimal policy
- state space
- reward function
- total reward
- transition matrices
- policy iteration
- decision theoretic planning
- dynamic programming
- planning under uncertainty
- dynamic environments
- reachability analysis
- factored mdps
- reinforcement learning algorithms
- partially observable
- expected reward
- average reward
- lower bound
- finite horizon
- markov decision process
- model based reinforcement learning
- average cost
- infinite horizon
- action sets
- loss function
- online convex optimization
- state and action spaces
- decision processes
- action space
- temporal difference
- multistage
- objective function