Year: 2016
Type: preprint
Abstract: This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic policy improvement can stabilize methods like LSPI by controlling the improvements' stochasticity. Additionally we show that a suitable representation of the value function also stabilizes the solution to some degree. The presented approach is simple and should also be easily transferable to more sophisticated algorithms like deep reinforcement learning.
Source: arXiv (Cornell University)
Authors: Böhmer, Wendelin, Guo, Rong, Obermayer, Klaus
Institution: Technische Universität Berlin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
Award: G5920221905
OpenAlex ID: W2562992078