Deep reinforcement learning a study of the CartPole problem /
One of the major challenges of artificial intelligence is to learn solving tasks which are considered to be challenging for even a human. Reinforcement learning is the most general learning framework among the three main type of learning methods: supervised, unsupervised and reinforcement learning....
Elmentve itt :
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| Dokumentumtípus: | Könyv része |
| Megjelent: |
2018
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| Sorozat: | Conference of PhD Students in Computer Science
11 |
| Kulcsszavak: | Számítástechnika - előadáskivonat, Algoritmus - előadáskivonat |
| Online Access: | http://acta.bibl.u-szeged.hu/61754 |
| Tartalmi kivonat: | One of the major challenges of artificial intelligence is to learn solving tasks which are considered to be challenging for even a human. Reinforcement learning is the most general learning framework among the three main type of learning methods: supervised, unsupervised and reinforcement learning. Most of the problems can easily fit into this framework. Experience shows that a lot of machine learning methods with non-linear function approximators suffers from instability regarding convergence. Reinforcement learning is more prone to diverge due to its ability to change the structure of its training data by modifying the way how it interacts with the environment. In this paper we investigate the divergence issue of DQN on the CartPole problem in terms of the algorithm’s parameters. Instead of the usual approach we do not focus on the successful trainings but instead we focus on the dark side where the algorithm fails on such an easy problem like CartPole. The motivation is to gain some further insight into the nature of the divergence issues on a specific problem. |
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| Terjedelem/Fizikai jellemzők: | 17-20 |