arXiv:1911.04868
Challenging On Car Racing Problem from OpenAI gym
원문 초록
This project challenges the car racing problem from OpenAI gym environment. The problem is very challenging since it requires computer to finish the continuous control task by learning from pixels. To tackle this challenging problem, we explored two approaches including evolutionary algorithm based genetic multi-layer perceptron and double deep Q-learning network. The result shows that the genetic multi-layer perceptron can converge fast but when training many episodes, double deep Q-learning can get better score. We analyze the result and draw a conclusion that for limited hardware resources, using genetic multi-layer perceptron sometimes can be more efficient.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network
SPEQ: Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning
Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios
A novel DDPG method with prioritized experience replay
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
토론
이 논문과 연결된 포럼 글이 아직 없습니다.