Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Task Independent Capsule-Based Agents for Deep Q-Learning
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better Performance
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
Deep Reinforcement Learning Based Power Allocation for D2D Network
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.