import gym
import time
from agent.DQN import DQN_Agent
from model.Network import DenseNet
from torch import nn
from common.Policy_for_DQN import EpsGreedyQPolicy
#%%
envID = 'D_place_action-v0'
env = gym.make(envID)
nowtime = time.strftime('%y%m%d%H%M', time.localtime())
path = "savedate" + '/' + envID + "-dqn-" + nowtime + '/'
#%%
policy = EpsGreedyQPolicy()
model = DenseNet(env.observation_space.shape[0],
env.action_space.n,
hidden_activate=nn.Tanh())
Agent = DQN_Agent(env, model, policy, gamma=0.90, lr=1e-3, path=path)
# Agent.train(max_step=1e6, render=False, verbose=2)
# Agent.save_weights(path)
#%%
path = "savedate" + '/' + envID + "-dqn-" + "2002191728"
Agent.load_weights(path)
Agent.test(max_step=10000, render=True, verbose=2)
import gym
import time
from agent.DQN import DQN_Agent
from model.Network import DenseNet
from torch import nn
from common.Policy_for_DQN import BoltzmannQPolicy
#%%
envID = "CartPole-v0"
env = gym.make(envID)
nowtime = time.strftime('%y%m%d%H%M', time.localtime())
path = "savedate" + '/' + envID + "dqn" + nowtime + '/'
#%%
policy = BoltzmannQPolicy()
model = DenseNet(env.observation_space.shape[0],
env.action_space.n,
hidden_activate=nn.Tanh())
Agent = DQN_Agent(env, model, policy, gamma=0.99, lr=1e-3, path=path)
Agent.train(max_step=100000, render=False, verbose=2)
Agent.test(max_step=10000, render=False, verbose=2)