def test_remote_worker_env(self):
config = pg.DEFAULT_CONFIG.copy()
config["remote_worker_envs"] = True
config["num_envs_per_worker"] = 4
# Simple string env definition (gym.make(...)).
config["env"] = "CartPole-v0"
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
# Using tune.register.
config["env"] = "cartpole"
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
# Using class directly.
config["env"] = RandomEnv
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
# Using class directly: Sub-class of gym.Env,
# which implements its own API.
config["env"] = NonVectorizedEnvToBeVectorizedIntoRemoteBaseEnv
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
def test_remote_worker_env_multi_agent(self):
config = pg.DEFAULT_CONFIG.copy()
config["remote_worker_envs"] = True
config["num_envs_per_worker"] = 4
# Full classpath provided.
config["env"] = "ray.rllib.examples.env.random_env.RandomMultiAgentEnv"
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
# Using tune.register.
config["env"] = "pistonball"
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
# Using class directly.
config["env"] = RandomMultiAgentEnv
trainer = pg.PGTrainer(config=config)
print(trainer.train())
trainer.stop()
