Examples of using Agit training
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import ray.rllib.agents.ppo as ppo
from ray.tune.logger import pretty_print
import os
import ray
The following switch allows the program to run locally or on the Agit distributed cluster without modifications.
if 'CLOUD_PROVIDER' in os.environ and os.environ['CLOUD_PROVIDER'] == 'Agit':
from agit import ray_init
config = ppo.DEFAULT_CONFIG.copy()
config["num_gpus"] = 0
config["num_workers"] = 1
config["eager"] = False
# Create a trainer that holds PPO policy for environment interaction.
trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")
checkpoint_path = ""
# Can optionally call trainer.restore(checkpoint_path) to load a checkpoint.
for i in range(1000):
# Perform one iteration of training the policy with PPO
result = trainer.train()
if i % 100 == 0:
checkpoint = trainer.save(checkpoint_path)
print("checkpoint saved at", checkpoint)