policy
Reinforcement-Learning-for-Rocket-Landing-Simulator
bartkw12 · PyTorch
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Overview
Name
Reinforcement-Learning-for-Rocket-Landing-Simulator
Author
bartkw12
Framework
PyTorch
License
unknown
Skill type
aerial
Evidence level
untested
Task description
Designed and trained three reinforcement learning agents — Q-Learning, Deep Q-Network (DQN), and REINFORCE — to land a rocket safely in a simulated hostile terrain using Gymnasium's LunarLander-v2 environment. Integrated key RL techniques such as experience replay, epsilon-greedy policy, and policy
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
3+17 mentioned but not in catalog yetCompatible environments
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Datasets that reference this policy
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