Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance.
Jakob J. HollensteinSayantan AuddyMatteo SaverianoErwan RenaudoJustus H. PiaterPublished in: Trans. Mach. Learn. Res. (2022)
Keyphrases
- action selection
- reinforcement learning
- active exploration
- action space
- temporal difference
- state space
- partially observable domains
- exploration exploitation
- noise model
- reward shaping
- random noise
- optimal policy
- noise level
- noise sensitivity
- agent learns
- dynamic programming
- machine learning
- multi agent
- multiscale
- autonomous learning
- fitted q iteration
- gaussian noise
- function approximation
- noise reduction
- additive noise
- model free
- learning classifier systems
- human actions
- noisy data
- missing data
- image restoration
- input data
- denoising
- learning process