def evaluate_actor_critic(params, path):
model = ActorCritic(params.stack_size, get_action_space())
model.load_state_dict(torch.load(path))
model.eval()
env = gym.make('CarRacing-v0')
env_wrapper = EnvironmentWrapper(env, params.stack_size)
total_reward = 0
num_of_episodes = 100
for episode in range(num_of_episodes):
state = env_wrapper.reset()
state = torch.Tensor([state])
done = False
score = 0
while not done:
probs, _, _ = model(state)
action = get_actions(probs)
state, reward, done = env_wrapper.step(action[0])
print(probs.detach().numpy(), "\n", action, reward)
state = torch.Tensor([state])
score += reward
env_wrapper.render()
print('Episode: {0} Score: {1:.2f}'.format(episode, score))
total_reward += score
return total_reward / num_of_episodes
def actor_critic_inference(params, path):
model = ActorCritic(params.stack_size, get_action_space())
model.load_state_dict(torch.load(path))
model.eval()
env = gym.make('CarRacing-v0')
env_wrapper = EnvironmentWrapper(env, params.stack_size)
state = env_wrapper.reset()
state = torch.Tensor([state])
done = False
total_score = 0
while not done:
probs, _, _ = model(state)
action = get_actions(probs)
print(action)
state, reward, done = env_wrapper.step(action[0])
state = torch.Tensor([state])
total_score += reward
env_wrapper.render()
return total_score