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zNMw3Q+A|(-{sp}J3(8hL(Ii0?E-z4~iw4(ch{P_vjq-&?VPxE2m`uN~QB|c>AotnF zCi(RvsRwK`Cle0n_~IG8hFDdmBy!*9=>-(2;y{md0cI#fRDhA8ksAskPdihEA)go+ zAV77bWTsAC_)#mLw_Vu;?xE8zVpqp8L{FqNe$pCC_<57ddDmDid?oxfdM4Q2B_$!e zwk?SGei3MP0F?v9?!$?X@b3NsWm1Ovwfu4ToNbfr`IfV+$Z+L8yLcNEB#7Jtp@r;##WE-!s^LVmBVG Vs;nF&g1#AnP*c)UERi=0`F}vra?bz& diff --git a/tests/parallel_api_check.py b/tests/parallel_api_check.py index bc684ba..94e6504 100644 --- a/tests/parallel_api_check.py +++ b/tests/parallel_api_check.py @@ -10,6 +10,7 @@ parallel_to_aec_wrapper, turn_based_aec_to_parallel_wrapper, ) +from generals.envs import PettingZooGenerals from pettingzoo.utils.env import ActionType, AgentID, ObsType, ParallelEnv from pettingzoo.utils.wrappers import BaseWrapper @@ -28,12 +29,22 @@ def sample_action( # pick random index of the mask with a 1, it should be 3 numbers valid_actions = np.argwhere(mask == 1) if len(valid_actions) == 0: - return (1, np.array([0, 0]), 0, 0) + return { + "pass": 1, + "cell": np.array([0, 0]), + "direction": 0, + "split": 0, + } action_index = np.random.choice(len(valid_actions)) action = valid_actions[action_index] cell = action[:2] direction = action[2] - return (0, cell, direction, 0) + return { + "pass": 0, + "cell": cell, + "direction": direction, + "split": 0, + } return env.action_space(agent).sample() @@ -128,14 +139,13 @@ def parallel_api_test(par_env: ParallelEnv, num_cycles=1000): if __name__ == "__main__": - mapper = GridFactory() - agent1 = AgentFactory.make_agent("expander", id="A") - agent2 = AgentFactory.make_agent("random", id="B") + agent1 = AgentFactory.make_agent("Expander", id="A") + agent2 = AgentFactory.make_agent("Random", id="B") agents = { agent1.id: agent1, agent2.id: agent2, } - env = gym.make("pz-generals-v0", agents=list(agents.keys()), grid_factory=mapper) + env = PettingZooGenerals(agents=agents, render_mode=None) # test the environment with parallel_api_test import time

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