RL-L: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios.
Zhutian ChenDaniele ChiappalupiTica LinYalong YangJohanna BeyerHanspeter PfisterPublished in: IEEE Trans. Vis. Comput. Graph. (2024)
Keyphrases
- reinforcement learning
- function approximation
- state space
- model free
- augmented reality
- temporal difference
- reinforcement learning algorithms
- real world
- learning process
- optimal policy
- learning algorithm
- reinforcement learning methods
- temporal difference learning
- optimal control
- markov decision processes
- transfer learning
- multi agent
- approximate dynamic programming
- continuous state
- dynamic programming
- learning classifier systems
- autonomous learning
- rl algorithms
- function approximators
- multi label
- action selection
- control policy
- policy iteration
- control problems
- learning capabilities
- dynamic environments
- policy gradient
- multi agent reinforcement learning