Power and accountability in reinforcement learning applications to environmental policy.
Melissa S. ChapmanCaleb ScovilleMarcus LapeyrolerieCarl BoettigerPublished in: CoRR (2022)
Keyphrases
- reinforcement learning
- optimal policy
- policy search
- markov decision process
- action selection
- partially observable environments
- reinforcement learning algorithms
- reinforcement learning problems
- actor critic
- function approximators
- state and action spaces
- policy gradient
- control policy
- function approximation
- partially observable
- markov decision problems
- power consumption
- reward function
- model free
- action space
- markov decision processes
- state space
- state action
- approximate dynamic programming
- policy iteration
- dynamic programming
- transition model
- policy evaluation
- machine learning
- control policies
- policy makers
- partially observable markov decision processes
- optimal control
- decision problems
- continuous state
- dynamic pricing
- average reward
- continuous state spaces
- control problems
- multi agent
- neural network
- policy gradient methods