| Autor: |
Abhinav Sharma, Ruchir Gupta, K. Lakshmanan, Atul Gupta |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Symmetry, Vol 13, Iss 7, p 1197 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2073-8994 |
| DOI: |
10.3390/sym13071197 |
| Popis: |
Reinforcement Learning (RL) enables an agent to learn control policies for achieving its long-term goals. One key parameter of RL algorithms is a discount factor that scales down future cost in the state’s current value estimate. This study introduces and analyses a transition-based discount factor in two model-free reinforcement learning algorithms: Q-learning and SARSA, and shows their convergence using the theory of stochastic approximation for finite state and action spaces. This causes an asymmetric discounting, favouring some transitions over others, which allows (1) faster convergence than constant discount factor variant of these algorithms, which is demonstrated by experiments on the Taxi domain and MountainCar environments; (2) provides better control over the RL agents to learn risk-averse or risk-taking policy, as demonstrated in a Cliff Walking experiment. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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