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
20anybotics-anymal-cnot in seedalohanot in seedgoogle-barkour-vbnot in seedboston-dynamics-spotnot in seedfranka-fr3not in seedgoogle-barkour-v0not in seedagilex-pipernot in seedberkeley-humanoidnot in seedbitcraze-crazyflie-2not in seedanybotics-anymal-bnot in seedagility-cassienot in seedarx-l5not in seedbooster-t1not in seedfranka-emika-pandanot in seedfranka-fr3-v2not in seeddynamixel-2rnot in seedflexiv-rizon4not in seedassetsnot in seedapptronik-apollonot in seedfourier-n1not in seed
Compatible environments
0No environments list DQL_agent yet.
Datasets that reference this policy
0No datasets reference DQL_agent yet.