TME on Evolutionary Algorithms

Author: S. Doncieux, 2019
University: Sorbonne Université

The objective of this practical course is to test evolutionary algorithms through available python frameworks and to make a first experiment on OpenAI Gym with a reinforcement learning problem. See the jupyter-notebook TME-IAR.ipynb for more details.

Dependencies

DEAP
pycma
OpenAI Gym

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commits
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
README.md		README.md
TME-IAR.ipynb		TME-IAR.ipynb
cmaes.py		cmaes.py
comparaison.py		comparaison.py
es.py		es.py
fixed_structure_nn_numpy.py		fixed_structure_nn_numpy.py
gym_cartpole.py		gym_cartpole.py
plot.py		plot.py

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

.ipynb_checkpoints

.ipynb_checkpoints

pycache

pycache

README.md

README.md

TME-IAR.ipynb

TME-IAR.ipynb

cmaes.py

cmaes.py

comparaison.py

comparaison.py

es.py

es.py

fixed_structure_nn_numpy.py

fixed_structure_nn_numpy.py

gym_cartpole.py

gym_cartpole.py

plot.py

plot.py

Repository files navigation

TME on Evolutionary Algorithms

Dependencies

About

Releases

Packages

Languages

AsmaBRZ/Evolutionary-algorithms

Folders and files

Latest commit

History

Repository files navigation

TME on Evolutionary Algorithms

Dependencies

About

Resources

Stars

Watchers

Forks

Languages