Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm
Not on the map. Why?
Abstract (arXiv)
Reinforcement Learning has achieved tremendous success in the many Atari games. In this paper we explored with the lunar lander environment and implemented classical methods including Q-Learning, SARSA, MC as well as tiling coding. We also implemented Neural Network based methods including DQN, Double DQN, Clipped DQN. On top of these, we proposed a new algorithm called Heuristic RL which utilizes heuristic to guide the early stage training while alleviating the introduced human bias. Our experiments showed promising results for our proposed methods in the lunar lander environment.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 17 Jun 2023.
Builds on
6 of 10 references.
References · 6
- 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2017Few-Shot Learning with Graph Neural Networkscited by 324
- 2018Reinforcement Learning with A* and a Deep Heuristiccited by 4
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Citing papers per year: 2026: 1.
1 paper.