policy
RL-CartPole-A2C
mahdiznaidi · PyTorch
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Overview
Name
RL-CartPole-A2C
Author
mahdiznaidi
Framework
PyTorch
License
unknown
Skill type
other
Evidence level
untested
Task description
CartPole-A2C is a reinforcement learning project that implements the Advantage Actor-Critic (A2C) algorithm to solve the classic CartPole environment from Gymnasium. The goal is to train an agent capable of balancing a pole on a moving cart by learning optimal policies through trial and error.
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
0No environments list RL-CartPole-A2C yet.
Datasets that reference this policy
0No datasets reference RL-CartPole-A2C yet.