run.py

# run this file to see how my trained models work.


from unityagents import UnityEnvironment
import numpy as np
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from collections import namedtuple,deque


# please write the path of Banana.exe here)
env = UnityEnvironment(file_name="Banana_Windows_x86_64/Banana_Windows_x86_64/Banana.exe")

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



from model import QNetwork
from bufer import Replay_Buffer
from Agent import Agent
agent = Agent(state_size = 37, action_size = 4, seed = 0)


agent.qnetwork_local.load_state_dict(torch.load('Banana_saved_model.pth'))

eps = 0.
scores = []
for i in range(5):

    env_info = env.reset(train_mode=True)[brain_name] # reset the environment
    state = env_info.vector_observations[0]            # get the current state
    score = 0                                          # initialize the score
    while True:
        action = agent.select_act(state,eps)           # 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
        if done:                                       # exit loop if episode finished
            break
    scores.append(score)
    #print("Score: {}".format(score))
print('Avg score:',np.mean(scores))