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test.py
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test.py
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import torch
import configparser
import argparse
from lib.algos.ppo import Agent
from lib.envs.SnakeEnvSP import SnakeEnv
parser = argparse.ArgumentParser()
parser.add_argument('-agent_name', help='Name of agent to load')
parser.add_argument('-num_games', type=int, default = 5, help='Number of games to test for')
args = parser.parse_args()
agent_name = args.agent_name
agent_path = 'agents/' + agent_name + '/'
config = configparser.ConfigParser()
config.read(agent_path + 'config.ini')
network_parameters = config['NETWORK_PARAMETERS']
env_parameters = config['ENV_PARAMETERS']
HIDDEN_SIZE = int(network_parameters['HIDDEN_SIZE'])
NUM_GAMES = args.num_games
env = SnakeEnv(env_parameters=env_parameters, renderID='Test', renderWait=100, channel_first=True)
agent = Agent(n_actions=env.action_space.n, agent_name=agent_name, input_channels=3, ppo_parameters=None, network_parameters=network_parameters)
agent.load_model(testing=True)
if __name__ == '__main__':
history = []
for _ in range(NUM_GAMES):
hidden = (torch.zeros(1, HIDDEN_SIZE).to(agent.device), torch.zeros(1, HIDDEN_SIZE).to(agent.device))
state = env.reset()
done = False
score = 0
while not done:
state = torch.FloatTensor(state).unsqueeze(0).to(agent.device)
action, _, value, hidden = agent.choose_action(state, hidden)
env.render()
env.renderVision()
next_state, reward, done, _ = env.step(action.cpu().numpy()[0])
state = next_state
score+=reward*(1-done)
history.append(score)
print(f'Scores for {NUM_GAMES} games:', history)