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import os
import gym
from tensorboardX import SummaryWriter
from torch.optim.lr_scheduler import LambdaLR
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs import BaseEnvManager, DingEnvWrapper
from ding.policy import DDPGPolicy
from ding.model import ContinuousQAC
from ding.utils import set_pkg_seed
from dizoo.classic_control.pendulum.envs import PendulumEnv
from dizoo.classic_control.pendulum.config.pendulum_td3_config import pendulum_td3_config
def main(cfg, seed=0):
cfg = compile_config(
cfg,
BaseEnvManager,
DDPGPolicy,
BaseLearner,
SampleSerialCollector,
InteractionSerialEvaluator,
AdvancedReplayBuffer,
save_cfg=True
)
# Set up envs for collection and evaluation
collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
collector_env = BaseEnvManager(
env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager
)
evaluator_env = BaseEnvManager(
env_fn=[lambda: PendulumEnv(cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager
)
# Set random seed for all package and instance
collector_env.seed(seed)
evaluator_env.seed(seed, dynamic_seed=False)
set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
# Set up RL Policy
model = ContinuousQAC(**cfg.policy.model)
policy = DDPGPolicy(cfg.policy, model=model)
# lr_scheduler demo
lr_scheduler = LambdaLR(
policy.learn_mode.get_attribute('optimizer_actor'), lr_lambda=lambda iters: min(1.0, 0.5 + 0.5 * iters / 1000)
)
# Set up collection, training and evaluation utilities
tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
collector = SampleSerialCollector(
cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
)
evaluator = InteractionSerialEvaluator(
cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
)
replay_buffer = AdvancedReplayBuffer(cfg.policy.other.replay_buffer, tb_logger, exp_name=cfg.exp_name)
# Training & Evaluation loop
while True:
# Evaluate at the beginning and with specific frequency
if evaluator.should_eval(learner.train_iter):
stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if stop:
break
# Collect data from environments
new_data = collector.collect(train_iter=learner.train_iter)
replay_buffer.push(new_data, cur_collector_envstep=collector.envstep)
# Train
for i in range(cfg.policy.learn.update_per_collect):
train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter)
if train_data is None:
break
learner.train(train_data, collector.envstep)
lr_scheduler.step()
tb_logger.add_scalar('other_iter/scheduled_lr', lr_scheduler.get_last_lr()[0], learner.train_iter)
if __name__ == "__main__":
main(pendulum_td3_config, seed=0)
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