This package provides implementations of the following multi-agent reinforcement learning environemnts:
Clone this repository.
git clone --recursive git@github.com:sisl/MADRL.gitAdd directories to PYTHONPATH
export PYTHONPATH=$(pwd):$(pwd)/rltools:$(pwd)/rllab:$PYTHONPATHInstall the required dependencies. Good idea is to look into rllab/environment.yml file if using anaconda distribution.
Example run with curriculum:
python3 runners/run_multiwalker.py rllab \ # Use rllab for training
--control decentralized \ # Decentralized training protocol
--policy_hidden 100,50,25 \ # Set MLP policy hidden layer sizes
--n_iter 200 \ # Number of iterations
--n_walkers 2 \ # Starting number of walkers
--batch_size 24000 \ # Number of rollout waypoints
--curriculum lessons/multiwalker/env.yamlPolicy definitions exist in rllab/sandbox/rocky/tf/policies.