policy
Playing-Pong-with-Deep-Reinforcement-Learning
junthbasnet · PyTorch
or hover any field below to flag it
Overview
Name
Playing-Pong-with-Deep-Reinforcement-Learning
Author
junthbasnet
Framework
PyTorch
License
MIT
Skill type
other
Evidence level
untested
Task description
🏓Deep learning model is presented to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estima
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 Playing-Pong-with-Deep-Reinforcement-Learning yet.
Datasets that reference this policy
0No datasets reference Playing-Pong-with-Deep-Reinforcement-Learning yet.