GitHub - machine-intelligence/Rainbow: Rainbow: Combining Improvements in Deep Reinforcement Learning

Data-efficient Rainbow [9] can be run using the following options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps):

python main.py --target-update 2000 \
               --T-max 100000 \
               --learn-start 1600 \
               --memory-capacity 100000 \
               --replay-frequency 1 \
               --multi-step 20 \
               --architecture data-efficient \
               --hidden-size 256 \
               --learning-rate 0.0001 \
               --evaluation-interval 10000

To install all dependencies with Anaconda run conda env create -f environment.yml and use source activate rainbow to activate the environment.