High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observations space. However, in real world settings, the environment is often non-stationary and subject to unpredictable, frequent changes. In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes. Then, we introduce an end-to-end model-free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations
Continual Reinforcement Learning in 3D Non-stationary Environments
2020-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
Springer Verlag | 2023
|CONTINUAL PROACTIVE LEARNING FOR AUTONOMOUS ROBOT AGENTS
Europäisches Patentamt | 2021
|CONTINUAL PROACTIVE LEARNING FOR AUTONOMOUS ROBOT AGENTS
Europäisches Patentamt | 2022
|