from pettingzoo.test import (
parallel_api_test,
parallel_seed_test,
max_cycles_test,
performance_benchmark,
)
I have a custom multiagent environment that extends ParallelEnv, and since I passed the parallel_api_test,
I plan to pass the other ones as well before starting training:
- parallel_seed_test
...
File "D:\anaconda3\Lib\site-packages\pettingzoo\test\seed_test.py", line 139, in parallel_seed_test
check_environment_deterministic_parallel(env1, env2, num_cycles)
File "D:\anaconda3\Lib\site-packages\pettingzoo\test\seed_test.py", line 108, in check_environment_deterministic_parallel
assert data_equivalence(actions1, actions2), "Incorrect action seeding"
I have no idea how to pass this one. I tried adding np.random.seed()
statements in my observation_space and action_space functions, but I don't know how to get deterministic actions. Please advise. Are there any steps I can follow to pass the seed test and make my environment results reproducible?
- max_cycles_test
...
File "D:\anaconda3\Lib\site-packages\pettingzoo\test\max_cycles_test.py", line 6, in max_cycles_test
parallel_env = mod.parallel_env(max_cycles=max_cycles)
^^^^^^^^^^^^^^^^
AttributeError: 'MultiAgentHighway' object has no attribute 'parallel_env'
I'm not sure how to use this? I have end_of_sim
as the maximum number of steps in the simulation before which the simulation is closed forcefully?
- performance_benchmark: Had to convert my
ParallelEnv
toAEC
withparallel_to_aec()
to use this.
2466.7955100048803 turns per second
123.33977550024402 cycles per second
Finished performance benchmark
How do I evaluate these numbers? Please advise.
Thank you in advance :)