def eval(model_type=model_type, model_path=model_path):
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
env = LunarLander()
if model_type == 'policy':
model = Policy(env.observation_dim, env.action_dim)
elif model_type == 'dqn':
model = Network(env.observation_dim, env.action_dim)
model.to(device)
model.load_state_dict(torch.load(model_path))
model.eval()
episodes = 50
wins = 0
frames = []
fuel_left = []
for i in range(episodes):
if i % 10 == 0:
print(f"On episode {i}")
frame_count = 0
env.reset()
state = env.get_state()
while True:
frame_count += 1
action = model(
torch.tensor(state, dtype=torch.float32,
device=device).unsqueeze(0)).argmax()
state, reward, done = env.step(action)
if done:
if env.won:
wins += 1
frames.append(frame_count)
fuel_left.append(env.rocket.fuel)
break
env.close()
if wins > 0:
print(f"wins: {wins}")
print(f"mean frames on wins {np.mean(frames)}")
print(f"std frames on wins {np.std(frames, ddof=1)}")
print(f"min frames on wins {np.min(frames)}")
print(f"max frames on wins {np.max(frames)}")
print(f"mean fuel on wins {np.mean(fuel_left)}")
print(f"std fuel on wins {np.std(fuel_left, ddof=1)}")
print(f"min fuel on wins {np.min(fuel_left)}")
print(f"max fuel on wins {np.max(fuel_left)}")
else:
print("The model had 0 wins. Statistics can't be calculated")
model_path = "policies/22-1-2021_13-44/policy0.tar"
env = LunarLander()
env.reset()
exit_program = False
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
if model_type == 'policy':
model = Policy(env.observation_dim, env.action_dim)
elif model_type == 'dqn':
model = Network(env.observation_dim, env.action_dim)
model.to(device)
model.load_state_dict(torch.load(model_path))
model.eval()
state = env.get_state()
while not exit_program:
env.render()
action = model(
torch.tensor(state, dtype=torch.float32,
device=device).unsqueeze(0)).argmax()
state, reward, done = env.step(action)
# Process game events
for event in pygame.event.get():
if event.type == pygame.QUIT:
