| Autor: |
Gadgil, Soham, Xin, Yunfeng, Xu, Chengzhe |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
SoutheastCon 2020 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Reinforcement Learning (RL) is an area of machine learning concerned with enabling an agent to navigate an environment with uncertainty in order to maximize some notion of cumulative long-term reward. In this paper, we implement and analyze two different RL techniques, Sarsa and Deep QLearning, on OpenAI Gym's LunarLander-v2 environment. We then introduce additional uncertainty to the original problem to test the robustness of the mentioned techniques. With our best models, we are able to achieve average rewards of 170+ with the Sarsa agent and 200+ with the Deep Q-Learning agent on the original problem. We also show that these techniques are able to overcome the additional uncertainities and achieve positive average rewards of 100+ with both agents. We then perform a comparative analysis of the two techniques to conclude which agent peforms better. |
| Databáze: |
arXiv |
| Externí odkaz: |
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