import numpy as np
from agents.DQNAgent import DQNAgent
from utils.plot import Plot
from environments.wrapper import EnvironmentWrapper
import os

#import models
ENV_NAME = "LunarLander-v2"
env = EnvironmentWrapper(ENV_NAME, max_ts=1000)
agent = DQNAgent(state_size=8, action_size=env.env.action_space.n, seed=0)

### Train the agent
#env.fit(agent, n_episodes=2000)

# See the results
#Plot.basic_plot(np.arange(len(env.scores)), env.scores, xlabel='Episode #', ylabel='Score')

env.load_model(agent, "./models/LunarLander-v2-4477.pth")
env.play(num_episodes=15, trained=False)


c83cc38200a9f78439692071fff35d2843bb4af8
<mask token>

<mask token>
env.load_model(agent, './models/LunarLander-v2-4477.pth')
env.play(num_episodes=15, trained=False)

<mask token>
ENV_NAME = 'LunarLander-v2'
env = EnvironmentWrapper(ENV_NAME, max_ts=1000)
agent = DQNAgent(state_size=8, action_size=env.env.action_space.n, seed=0)
env.load_model(agent, './models/LunarLander-v2-4477.pth')
env.play(num_episodes=15, trained=False)

import numpy as np
from agents.DQNAgent import DQNAgent
from utils.plot import Plot
from environments.wrapper import EnvironmentWrapper
import os
ENV_NAME = 'LunarLander-v2'
env = EnvironmentWrapper(ENV_NAME, max_ts=1000)
agent = DQNAgent(state_size=8, action_size=env.env.action_space.n, seed=0)
env.load_model(agent, './models/LunarLander-v2-4477.pth')
env.play(num_episodes=15, trained=False)

import numpy as np
from agents.DQNAgent import DQNAgent
from utils.plot import Plot
from environments.wrapper import EnvironmentWrapper
import os

#import models
ENV_NAME = "LunarLander-v2"
env = EnvironmentWrapper(ENV_NAME, max_ts=1000)
agent = DQNAgent(state_size=8, action_size=env.env.action_space.n, seed=0)

### Train the agent
#env.fit(agent, n_episodes=2000)

# See the results
#Plot.basic_plot(np.arange(len(env.scores)), env.scores, xlabel='Episode #', ylabel='Score')

env.load_model(agent, "./models/LunarLander-v2-4477.pth")
env.play(num_episodes=15, trained=False)

