import gym
import torch
from env_wrappers import ActionNormalizedEnv
from models import DDPG_Actor
model_name = 'ddpg_01'
env_id = "Pendulum-v0"
identity = model_name + '_' + env_id
env = ActionNormalizedEnv(gym.make(env_id))
obs_size = env.observation_space.shape[0]
act_size = env.action_space.shape[0]
act_net = DDPG_Actor(obs_size, act_size)
act_net.load_state_dict(torch.load(identity + '_act.pth'))
def test_policy(actor, env, vis=False, n_episodes=2, max_len=500):
returns = []
for i_episode in range(n_episodes):
state = env.reset()
if vis: env.render()
episode_return = 0
for t in range(max_len):
action = actor.get_action([state])
state, reward, done, _ = env.step(action)
episode_return += reward
if vis: env.render()
if done:
returns.append(episode_return)
break
import numpy as np import torch.optim as optim from env_wrappers import ActionNormalizedEnv from models import * from ou import OUNoise from replay_buffer import ReplayBuffer from utils import * GAMMA = 0.99 SOFT_TAU = 1e-2 model_name = 'ddpg_01' env_id = "Pendulum-v0" identity = model_name + '_' + env_id env = ActionNormalizedEnv(gym.make(env_id)) ou_noise = OUNoise(env.action_space) obs_size = env.observation_space.shape[0] act_size = env.action_space.shape[0] act_net = DDPG_Actor(obs_size, act_size) cri_net = DDPG_Critic(obs_size, act_size) act_net_t = DDPG_Actor(obs_size, act_size) cri_net_t = DDPG_Critic(obs_size, act_size) hard_update(act_net_t, act_net) hard_update(cri_net_t, cri_net) mse_criterion = nn.MSELoss()
def _thunk():
env = gym.make(env_id)
return ActionNormalizedEnv(env)