def create_collector_env_gym(seed):
options = {
"world_dim": int(16),
"init_locator_type": int(3),
"num_goals_green": int(1),
"num_goals_red": int(1),
"num_resources_green": int(0),
"num_resources_red": int(0),
"num_fuel": int(0),
"num_obstacles": int(2),
"goal_max": 20.0,
"goal_init": 0.0,
"agent_max_fuel": 100.0,
"agent_init_fuel": 100.0,
"agent_max_resources": 100.0,
"agent_init_resources_green": 20.0,
"agent_init_resources_red": 10.0,
}
kwargs = {
"start_level":
seed if seed is not None else 102,
"num_levels":
10,
"additional_obs_spaces": [
ProcgenEnv.C_Space("state_ship", False, (9, ), float, (-1e6, 1e6)),
ProcgenEnv.C_Space(
"state_goals", False,
((options["num_goals_green"] + options["num_goals_red"]) *
4, ), float, (-1e6, 1e6)),
ProcgenEnv.C_Space("state_resources", False,
((options["num_resources_green"] +
options["num_resources_red"]) * 4, ), float,
(-1e6, 1e6)),
ProcgenEnv.C_Space("state_obstacles", False,
(options["num_obstacles"] * 3, ), float,
(-1e6, 1e6))
],
'max_episodes_per_game':
0,
"options":
options
}
# env = gym.make('procgen:procgen-collector-v0',**kwargs)
env = ProcgenEnv(num_envs=9, env_name="collector", **kwargs)
dtv = DictToVec([
space.name for space in kwargs["additional_obs_spaces"]
if np.prod(space.shape) > 0
])
env = StateTransformerEnv(env, dtv)
return env
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--vision",
choices=["agent", "human"],
default="human")
parser.add_argument("--record-dir", help="directory to record movies to")
parser.add_argument(
"--distribution-mode",
default="hard",
help="which distribution mode to use for the level generation")
parser.add_argument("--level-seed",
type=int,
help="select an individual level to use")
parser.add_argument("--use-generated-assets",
help="using autogenerated assets",
choices=["yes", "no"],
default="no")
args = parser.parse_args()
kwargs = {"distribution_mode": args.distribution_mode}
kwargs["use_generated_assets"] = True if (args.use_generated_assets
== "yes") else False
if args.level_seed is not None:
kwargs["start_level"] = args.level_seed
kwargs["num_levels"] = 1
world_dim = int(10)
kwargs["additional_info_spaces"] = [
ProcgenEnv.C_Space("state", False, (7 + world_dim * world_dim, ),
bytes, (0, 255))
]
kwargs["options"] = {
'world_dim': world_dim,
'wall_chance': 0.5,
'fire_chance': 0.3,
'water_chance': 0.2,
'num_keys': int(2),
'num_doors': int(1),
'with_grid_steps': True,
'completion_bonus': 10.0,
'fire_bonus': -5.0,
'water_bonus': -2.0,
'action_bonus': -1.0,
}
ia = ProcgenInteractive(args.vision, True, env_name="heistpp", **kwargs)
ia.skip_info_out("state")
step_cb = HeistppStatePlotter(world_dim, 1)
ia.add_step_callback(step_cb)
ia.run(record_dir=args.record_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--vision", choices=["agent", "human"], default="human")
parser.add_argument("--record-dir", help="directory to record movies to")
parser.add_argument("--distribution-mode", default="hard", help="which distribution mode to use for the level generation")
parser.add_argument("--level-seed", type=int, help="select an individual level to use")
parser.add_argument("--use-generated-assets", help="using autogenerated assets", choices=["yes","no"], default="no")
args = parser.parse_args()
kwargs = {"distribution_mode": args.distribution_mode}
kwargs["use_generated_assets"] = True if (args.use_generated_assets == "yes") else False
if args.level_seed is not None:
kwargs["start_level"] = args.level_seed
kwargs["num_levels"] = 1
world_dim = int(10)
kwargs["additional_info_spaces"] = [ProcgenEnv.C_Space("state", False, (7+world_dim*world_dim,), bytes, (0,255))]
kwargs["options"] = {
'world_dim':world_dim,
'wall_chance':0.5,
'fire_chance':0.3,
'water_chance':0.2,
'num_keys':int(2),
'num_doors':int(1),
'with_grid_steps':True,
'completion_bonus':10.0,
'fire_bonus':-5.0,
'water_bonus':-2.0,
'action_bonus':-1.0,
}
env = gym.make('procgen:procgen-heistpp-v0')
# env = ProcgenEnv(num_envs=1, env_name="heistpp", **kwargs)
obs = env.reset()
step = 0
while True:
env.render()
obs, rew, done, info = env.step(np.array([env.action_space.sample()]))
print(f"step {step} reward {rew} done {done}")
# print(info[0]['state'])
step += 1
if done:
break
env.close()
