main.py

from unityagents import UnityEnvironment
import numpy as np

## Additional Package
import torch
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
# %matplotlib inline
from agent import Agent

# please do not modify the line below
env = UnityEnvironment(file_name="Banana_Linux/Banana.x86_64")


# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]

# reset the environment
env_info = env.reset(train_mode=True)[brain_name]

# number of agents in the environment
print('Number of agents:', len(env_info.agents))

# number of actions
action_size = brain.vector_action_space_size
print('Number of actions:', action_size)

# examine the state space 
state = env_info.vector_observations[0]
print('States look like:', state)
state_size = len(state)
print('States have length:', state_size)

agent = Agent(state_size=state_size, action_size=action_size, seed=0)
agent.qnetwork_local.load_state_dict(torch.load('checkpoint.pth'))

import time
time.sleep(5)
env_info = env.reset(train_mode=False)[brain_name] # reset the environment
state = env_info.vector_observations[0]            # get the current state
score = 0                                          # initialize the score
while True:
    action = np.int16(agent.act(state)).item()     # select an action
    env_info = env.step(action)[brain_name]        # send the action to the environment
    next_state = env_info.vector_observations[0]   # get the next state
    reward = env_info.rewards[0]                   # get the reward
    done = env_info.local_done[0]                  # see if episode has finished
    score += reward                                # update the score
    state = next_state                             # roll over the state to next time step
    time.sleep(0.05)
    if done:                                       # exit loop if episode finished
        break
    
print("Score: {}".format(score))