Deep Distributional Reinforcement Learning Based High-Level Driving Policy Determination.
Kyushik MinHayoung KimKunsoo HuhPublished in: IEEE Trans. Intell. Veh. (2019)
Keyphrases
- reinforcement learning
- high level
- optimal policy
- policy search
- low level
- action selection
- markov decision process
- function approximators
- reward function
- actor critic
- state and action spaces
- function approximation
- partially observable environments
- control policies
- policy gradient
- markov decision processes
- state action
- action space
- markov decision problems
- partially observable
- higher level
- co occurrence
- policy iteration
- decision problems
- state space
- control policy
- approximate dynamic programming
- programming language
- reinforcement learning algorithms
- lower level
- reinforcement learning problems
- dynamic programming
- partially observable markov decision processes
- temporal difference
- model free reinforcement learning
- agent learns
- policy evaluation
- deep learning
- learning algorithm
- finite state
- multi agent
- low level features
- sufficient conditions
- policy gradient methods
- partially observable domains
- source code
- neural network
- rl algorithms
- transition model
- optimal control
- infinite horizon
- model free
- continuous state
- average cost