how to use the trainded model?

[suoer95]

In the following code (CybORG/Agents/Wrappers)/RLLibWrapper.py), we can get a trainded model, but I don't know how to use the model in a new env. Could you give me some suggestion? Thanks for the info!
`import inspect
import numpy as np
from ray.rllib.agents import ppo
from ray.rllib.env import ParallelPettingZooEnv
from ray.tune import register_env
from CybORG import CybORG
from CybORG.Agents import B_lineAgent, GreenAgent
from CybORG.Agents.Wrappers import ChallengeWrapper

from CybORG.Agents.Wrappers.PettingZooParallelWrapper import PettingZooParallelWrapper
from CybORG.Simulator.Scenarios import FileReaderScenarioGenerator, DroneSwarmScenarioGenerator

class RLLibWrapper(ChallengeWrapper):
def init(self, agent_name, env, reward_threshold=None, max_steps=None):
super().init(agent_name, env, reward_threshold, max_steps)

def step(self, action=None):
    obs, reward, done, info = self.env.step(action=action)
    self.step_counter += 1
    if self.max_steps is not None and self.step_counter >= self.max_steps:
        done = True
    return np.float32(obs), reward, done, info

def reset(self):
    self.step_counter = 0
    obs = self.env.reset()
    return np.float32(obs)

def env_creator_CC1(env_config: dict):
path = str(inspect.getfile(CybORG))
path = path[:-7] + f'/Simulator/Scenarios/scenario_files/Scenario1b.yaml'
sg = FileReaderScenarioGenerator(path)
agents = {"Red": B_lineAgent(), "Green": GreenAgent()}
cyborg = CybORG(scenario_generator=sg, environment='sim', agents=agents)
env = RLLibWrapper(env=cyborg, agent_name="Blue", max_steps=100)
return env

def env_creator_CC2(env_config: dict):
path = str(inspect.getfile(CybORG))
path = path[:-7] + f'/Simulator/Scenarios/scenario_files/Scenario2.yaml'
sg = FileReaderScenarioGenerator(path)
agents = {"Red": B_lineAgent(), "Green": GreenAgent()}
cyborg = CybORG(scenario_generator=sg, environment='sim', agents=agents)
env = RLLibWrapper(env=cyborg, agent_name="Blue", max_steps=100)
return env

def env_creator_CC3(env_config: dict):
sg = DroneSwarmScenarioGenerator()
cyborg = CybORG(scenario_generator=sg, environment='sim')
env = ParallelPettingZooEnv(PettingZooParallelWrapper(env=cyborg))
return env

def print_results(results_dict):
train_iter = results_dict["training_iteration"]
r_mean = results_dict["episode_reward_mean"]
r_max = results_dict["episode_reward_max"]
r_min = results_dict["episode_reward_min"]
print(f"{train_iter:4d} \tr_mean: {r_mean:.1f} \tr_max: {r_max:.1f} \tr_min: {r_min: .1f}")

if name == "main":
register_env(name="CC1", env_creator=env_creator_CC1)
register_env(name="CC2", env_creator=env_creator_CC2)
register_env(name="CC3", env_creator=env_creator_CC3)
config = ppo.DEFAULT_CONFIG.copy()
for env in ['CC1', 'CC2', 'CC3']:
agent = ppo.PPOTrainer(config=config, env=env)

    train_steps = 1e2
    total_steps = 0
    while total_steps < train_steps:
        results = agent.train()
        print_results(results)
        total_steps = results["timesteps_total"]`