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Repository for project "Optimistic Approximate Posterior Sampling for Exploration in Distributional Reinforcement Learning". This is a project for the Foundations of Reinforcement Learning course at ETH Zurich.

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Optimistic Approximate Posterior Sampling for Exploration in Distributional Reinforcement Learning

gymnasium supports and tests only up to python 3.11, so setup the conda environment with:

conda create --name forl-distributional python=3.11
conda activate forl-distributional
pip install swig
pip install -r requirements.txt

The following commands might be necessary:

pip install "gymnasium[all]"
pip install "gymnasium[accept-rom-license]"

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Repository for project "Optimistic Approximate Posterior Sampling for Exploration in Distributional Reinforcement Learning". This is a project for the Foundations of Reinforcement Learning course at ETH Zurich.

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