arXiv:2003.09579 공식 코드 · Papers with Code

FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques

Tai Vu, Leon Tran

제출 2020-03-21

원문 초록

Reinforcement learning is one of the most popular approaches for automated game playing. This method allows an agent to estimate the expected utility of its state in order to make optimal actions in an unknown environment. We seek to apply reinforcement learning algorithms to the game Flappy Bird. We implement SARSA and Q-Learning with some modifications such as $\epsilon$-greedy policy, discretization and backward updates. We find that SARSA and Q-Learning outperform the baseline, regularly achieving scores of 1400+, with the highest in-game score of 2069.

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arXiv:2506.15273공통 태그 reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Q-Learning 등록 2025-06-18

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access

논문 ID: publication/3577648공통 태그 reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Q-Learning 등록 2022-01-11

Task Independent Capsule-Based Agents for Deep Q-Learning

논문 ID: publication/3491416공통 태그 reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Q-Learning 등록 2021-02-01

Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better Performance

논문 ID: hash/7744129공통 태그 reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Q-Learning 등록 2020-12-01

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