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import os
import gym
import gym_hybrid
from tensorboardX import SummaryWriter
from ding.config import compile_config
from ding.worker import BaseLearner, SampleSerialCollector, InteractionSerialEvaluator, AdvancedReplayBuffer
from ding.envs import BaseEnvManager
from ding.policy import DDPGPolicy
from ding.model import ContinuousQAC
from ding.utils import set_pkg_seed
from ding.rl_utils import get_epsilon_greedy_fn
from dizoo.gym_hybrid.envs.gym_hybrid_env import GymHybridEnv
from dizoo.gym_hybrid.config.gym_hybrid_ddpg_config import gym_hybrid_ddpg_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
# You can either use `PendulumEnv` or `DingEnvWrapper` to make a pendulum env and therefore an env manager.
# == Use `DingEnvWrapper`
collector_env = BaseEnvManager(
env_fn=[lambda: GymHybridEnv(cfg=cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager
)
evaluator_env = BaseEnvManager(
env_fn=[lambda: GymHybridEnv(cfg=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)
# 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)
# Set up other modules, etc. epsilon greedy
eps_cfg = cfg.policy.other.eps
epsilon_greedy = get_epsilon_greedy_fn(eps_cfg.start, eps_cfg.end, eps_cfg.decay, eps_cfg.type)
# 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
# Update other modules
eps = epsilon_greedy(collector.envstep)
# Collect data from environments
new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs={'eps': eps})
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)
# evaluate
evaluator_env = BaseEnvManager(
env_fn=[lambda: GymHybridEnv(cfg=cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager
)
evaluator_env.enable_save_replay(cfg.env.replay_path) # switch save replay interface
evaluator = InteractionSerialEvaluator(
cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
)
evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
if __name__ == "__main__":
main(gym_hybrid_ddpg_config, seed=0)
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