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Study Model-Based Policy Optimization by varying the model estimator classes (e.g Decision Trees vs MLP)

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KohlerHECTOR/Tree-MBPO

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Install scikit-learn and SB3

pip3 install -r requirements.txt

trees-mlp

trees-mlp-times

trees

Available Models are Decision Trees, best CV Trees, and MLPs

Available Policy Optim Algos are SAC and TD3

Launch MBPO for 100 iterations on InvertedPendulum with Decision Trees as Model estimators and SAC as policy optim. Results are saved in 'Experience_Results/pendul-tree-sac/':

python3 experience.py InvertedPendulum-v4 tree sac 100 pendul-tree-sac

Launch MBPO for 100 iterations on InvertedPendulum with 2x64 MLP as Model estimators and SAC as policy optim. Results are saved in 'Experience_Results/pendul-mlp-sac/':

python3 experience.py InvertedPendulum-v4 mlp sac 100 pendul-mlp-sac

Save Plots of comparisons 'Experience_Results/Comparison-date-time/':

python3 compare_experiences.py pendul-tree-sac pendul-mlp-sac

Save Plots of results in 'Experience_Results/pendul-tree-sac/':

python3 plot_experience.py pendul-tree-sac

MBPO: https://arxiv.org/abs/1906.08253

MBPO-structure MBPO-rollout

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Study Model-Based Policy Optimization by varying the model estimator classes (e.g Decision Trees vs MLP)

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