def trainer(fargs):
trainer_id, args = fargs
print("Trainer id", trainer_id, "started")
# Create a Gym environment
env = gym.make(args.env)
# Set maximum episode length
if args.episode_steps is not None:
env._max_episode_steps = args.episode_steps
# Get dimensionalities of actions and observations
action_space_dim = get_space_dim(env.action_space)
observation_space_dim = get_space_dim(env.observation_space)
# Instantiate agent and its policy
policy = Policy(observation_space_dim, action_space_dim)
agent = Agent(policy)
training_history = train(agent,
env,
args.train_episodes,
silent=True,
train_run_id=trainer_id,
early_stop=False)
print("Trainer id", trainer_id, "finished")
return training_history
def trainer(args):
trainer_id, env = args
print("Trainer id", trainer_id, "started")
training_history = train(env, False, trainer_id)
print("Trainer id", trainer_id, "finished")
return training_history
