policy
reinforcementlearningmario
fahimaqil · PyTorch
or hover any field below to flag it
Overview
Name
reinforcementlearningmario
Author
fahimaqil
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
The aim of this project is to implement a state-of-the-art Deep Reinforcement Learning approach which is Proximal Policy Optimization (PPO) to train an agent to complete the first level of World 1 in Super Mario Bros.
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 reinforcementlearningmario yet.
Datasets that reference this policy
0No datasets reference reinforcementlearningmario yet.