policy
marioAI-Gaming-Reinforcement-Learning
ruchitapaithankar15 · PyTorch
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Overview
Name
marioAI-Gaming-Reinforcement-Learning
Author
ruchitapaithankar15
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
Built and trained a model using OpenAI gym, NES emulator to play Super Mario. Optimized the model using preprocessing techniques and vectorization. The algorithm used is PPO (Proximal Policy Optimal) along with Reinforcement Learning.
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 marioAI-Gaming-Reinforcement-Learning yet.
Datasets that reference this policy
0No datasets reference marioAI-Gaming-Reinforcement-Learning yet.