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Combined Model-Based and Inverse Reinforcement Learning for Generalization

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DreamBC: Dreaming with Bisimulation for Control

Here alg = dreamer,dreambc

Training on walker-walk: python alg_main.py --env walker-walk

Task Generalization on walker-stand: python alg_main.py --env walker-stand --model results/walker-walk/alg/models.pth

Feature Generalization on walker-walk: python alg_main.py --env walker-walk --img-source color --id alg-color

Render Episodes: python alg_main.py --env walker-walk --model results/walker-walk/alg/models.pth --agent-model results/walker-walk/alg/models.pth --test --test-episodes 5 --video

TODO: Switch to matplotlib .png from plotly .html Add functionality for multiple runs Rerun experiments with multiple seeds

Citations

PlaNet: Paper Code PyTorch

Dreamer: Paper Code PyTorch

DBC: Paper Code

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