The repository for paper: [Graph learning-based generation of abstractions for reinforcement learning]

The evaluations involve two types of tasks: flag-collection and navigation.

In flag-collection domain, the agent is tasked to traverse the environment to collect 3 flags and then bringen back to the goal state.

In navigation domain, the agent just need to find the optimal path from the start state to the goal state.

Instructions for tasks of flag-collection

Use graph-based AMDP, with stochasticity config: by the interval of 1 step, 50% of doors are closed with probability of 25%. To remove stochasticity, set the first argument for --stochasticity to 0.0.

python entrance.py --approach topology --maze basic --big 1 --e_eps 1000 -mm 100 --q_eps 500 --repetitions 10 --rep_size 128 --win_size 50 --numbers_of_clusters 9 16 --stochasticity 0.5 0.25 1 --print_to_file

Use uniform AMDP

python entrance.py --approach uniform --maze basic --big 1 --e_eps 1000 -mm 100 --q_eps 500 --repetitions 10 --numbers_of_clusters 9 16  --print_to_file

Instructions for tasks of navigation

Use graph-based AMDP

python entrance.py --approach topology --maze basic --big 1 --e_eps 1000 -mm 100 --q_eps 500 --repetitions 10 --rep_size 128 --win_size 50 --numbers_of_clusters 9 16 --print_to_file

Use uniform AMDP

python entrance.py --approach topology --maze basic --big 1 --e_eps 1000 -mm 100 --q_eps 500 --repetitions 10 --numbers_of_clusters 9 16 --print_to_file