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install:
cd gym_city/envs/micropolis/MicropolisCore; make; sudo make install
######### Micropolis #########
MP_res_FC:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/MicropolisEnv-v0_w16_200s_resOnlyThic' --poet --map-w 16 --random-b --random-t
MP_res_FC_w32:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/MicropolisEnv-v0_w16_200s_resOnlyThic' --poet --map-w 32 --random-t
condos:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/MicropolisEnv-v0_w16_200s_resOnlyThic' --map-width 64 --render --model FullyConv --poet --random-t
###
MP_SC_w16:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv_w16/MicropolisEnv-v0_MP0' --map-w 16 --val-kern 3
MP_SC_w32:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv_w16/MicropolisEnv-v0_MP0' --map-w 32 --val-kern 3 --random-b
bad_traffic:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/MicropolisEnv-v0_w16_300s_trafficVec' --random-t --non-det
nice_mix:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FractalNet-5recs_intra_inter_drop/MicropolisEnv-v0_w16_200s_MP0' --n-chan 32 --active-col 0 --map-width 20
nice_mix_w32:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FractalNet-5recs_intra_inter_drop/MicropolisEnv-v0_w16_200s_MP0' --n-chan 32 --active-col 0 --map-width 32
######### Game of Life #########
GoL_SC:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/GameOfLifeEnv-v0_w16_200s_MP_0' --n-chan 32 --val-kern 2 --model FullyConv --map-width 16 --render --max-step 300 --n-recs 5
GoL_SC_w32:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/GameOfLifeEnv-v0_w16_200s_MP_0' --n-chan 32 --val-kern 2 --model FullyConv --map-width 16 --render --map-width 32 --max-step 700
GoL_SC_w64:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/GameOfLifeEnv-v0_w16_200s_MP_0' --n-chan 32 --val-kern 2 --model FullyConv --map-width 16 --render --map-width 64 --max-step 2000
big_life:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FullyConv/GameOfLifeEnv-v0_w16_200s_MP_0' --n-chan 32 --val-kern 2 --model FullyConv --render --map-width 128 --max-step 5000 --prob-life
######## Power Puzzle ########
powerless:
python3 enjoy.py --load-dir '/home/sme/gym-city/trained_models/a2c_FractalNet-5recs_intra_drop/MicropolisEnv-v0_w16_200s_PP1' --render --n-chan 32 --map-width 16