# env = make_vec_env(env_id, n_envs=1, seed=0)
# env = VecCheckNan(env, raise_exception=True)
# env = check_env(env)
# The noise objects for DDPG
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=.1 * np.ones(n_actions))
# model = TD3(MlpPolicy, env, action_noise=action_noise, verbose=1, tensorboard_log="./TD3_spm_v2_SOC_point5_two_state/")
model = DDPG(MlpPolicy, env, action_noise=action_noise, verbose=1, tensorboard_log="./DDPG_spm_v2_SOC_point5_two_state/")
model.learn(total_timesteps=25000, tb_log_name='DDPG_test_run_3_SOCpoint5_two_state')
# model.save('DDPG_test_3_SOC_point5_two_states')
#
#
# model.load('DDPG_test_2_SOC_point5_two_states')
mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10)
print("Mean Reward = ", mean_reward)
epsi_sp_list = []
action_list = []
soc_list = []
Concentration_list = []
Concentration_list1 = []
obs = env.reset()
for _ in range(3600):
action, _states = model.predict(obs, deterministic=True)
obs, rewards, done, info = env.step(action)
lr = 3e-4
# Instantiate Model
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=-30 * np.zeros(n_actions),
sigma=.75 * np.ones(n_actions))
model = DDPG('MlpPolicy', env, action_noise=action_noise, verbose=1)
# model = PPO('MlpPolicy', env, tensorboard_log=log_dir)
# Train OR Load Model
model.learn(total_timesteps=25000)
# model.save(model_dir_description)
mean_reward, std_reward = evaluate_policy(model,
model.get_env(),
n_eval_episodes=10)
print("Mean Reward = ", mean_reward)
epsi_sp_list = []
action_list = []
soc_list = []
Concentration_list = []
Concentration_list1 = []
obs = env.reset()
for _ in range(3600):
action, _states = model.predict(obs)
obs, rewards, done, info = env.step(action)
import gym
import numpy as np
from stable_baselines3 import DDPG
from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
env = gym.make('MountainCarContinuous-v0')
# The noise objects for DDPG
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=np.zeros(n_actions),
sigma=0.1 * np.ones(n_actions))
model = DDPG('MlpPolicy', env, action_noise=action_noise, verbose=1)
model.learn(total_timesteps=1000000, log_interval=10)
model.save("ddpg_pendulum")
env = model.get_env()
del model # remove to demonstrate saving and loading
model = DDPG.load("ddpg_pendulum")
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
