policy

DQL_agent

Vishwanatha-14 · PyTorch

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Overview

Name
DQL_agent
Author
Vishwanatha-14
Framework
PyTorch
License
unknown
Skill type
aerial
Evidence level
untested
Task description
In this project, I developed an autonomous agent using reinforcement learning (RL) to master the task of safely landing a spacecraft on the moon. The agent learns optimal control policies by interacting with a simulated lunar environment, adjusting its thrust and orientation to achieve a smooth, con

Spaces

Action space
other · 0-dim · 0Hz
Observation space
  • type: other

Links

HuggingFace repo
null
Paper (arXiv)
null

Compatible robots

20

Compatible environments

0

No environments list DQL_agent yet.

Datasets that reference this policy

0

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