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| import numpy as np | |
| import utils | |
| import os | |
| import pickle | |
| import torch | |
| class AgentWrap: | |
| """ Handles action selection without gradient updates for proper testing """ | |
| def __init__(self, acmodel, preprocess_obss, device, num_envs=1, argmax=False): | |
| self.preprocess_obss = preprocess_obss | |
| self.acmodel = acmodel | |
| self.device = device | |
| self.argmax = argmax | |
| self.num_envs = num_envs | |
| if self.acmodel.recurrent: | |
| self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size, device=self.device) | |
| def get_actions(self, obss): | |
| preprocessed_obss = self.preprocess_obss(obss, device=self.device) | |
| with torch.no_grad(): | |
| if self.acmodel.recurrent: | |
| dist, _, self.memories = self.acmodel(preprocessed_obss, self.memories) | |
| else: | |
| dist, _ = self.acmodel(preprocessed_obss) | |
| if isinstance(dist, torch.distributions.Distribution): | |
| if self.argmax: | |
| actions = dist.probs.max(1, keepdim=True)[1] | |
| else: | |
| actions = dist.sample() | |
| else: | |
| if self.argmax: | |
| actions = torch.stack([d.probs.max(1)[1] for d in dist], dim=1) | |
| else: | |
| actions = torch.stack([d.sample() for d in dist], dim=1) | |
| return self.acmodel.construct_final_action(actions.cpu().numpy()) | |
| def get_action(self, obs): | |
| return self.get_actions([obs])[0] | |
| def analyze_feedbacks(self, rewards, dones): | |
| if self.acmodel.recurrent: | |
| masks = 1 - torch.tensor(dones, dtype=torch.float, device=self.device).unsqueeze(1) | |
| self.memories *= masks | |
| def analyze_feedback(self, reward, done): | |
| return self.analyze_feedbacks([reward], [done]) | |
| class Tester: | |
| def __init__(self, env_args, seed, episodes, save_path, acmodel, preprocess_obss, device): | |
| self.envs = [utils.make_env( | |
| **env_args | |
| ) for _ in range(episodes)] | |
| self.seed = seed | |
| self.episodes = episodes | |
| self.ep_counter = 0 | |
| self.savefile = save_path + "/testing_{}.pkl".format(self.envs[0].spec.id) | |
| print("Testing log: ", self.savefile) | |
| self.stats_dict = {"test_rewards": [], "test_success_rates": [], "test_step_nb": []} | |
| self.agent = AgentWrap(acmodel, preprocess_obss, device) | |
| def test_agent(self, num_frames): | |
| self.agent.acmodel.eval() | |
| # set seed | |
| # self.env.seed(self.seed) | |
| # save test time (nb training steps) | |
| self.stats_dict['test_step_nb'].append(num_frames) | |
| rewards = [] | |
| success_rates = [] | |
| # cols = [] | |
| # s = "-".join([e.current_env.marble.color for e in self.envs]) | |
| # print("s:", s) | |
| for episode in range(self.episodes): | |
| # self.envs[episode].seed(self.seed) | |
| self.envs[episode].seed(episode) | |
| # print("current_seed", np.random.get_state()[1][0]) | |
| obs = self.envs[episode].reset() | |
| # cols.append(self.envs[episode].current_env.marble.color) | |
| # cols.append(str(self.envs[episode].current_env.marble.cur_pos)) | |
| done = False | |
| while not done: | |
| action = self.agent.get_action(obs) | |
| obs, reward, done, info = self.envs[episode].step(action) | |
| self.agent.analyze_feedback(reward, done) | |
| if done: | |
| rewards.append(reward) | |
| success_rates.append(info['success']) | |
| break | |
| # from hashlib import md5 | |
| # hash_string = "-".join(cols).encode() | |
| # print('hs:', hash_string[:20]) | |
| # print("hash test envs:", md5(hash_string).hexdigest()) | |
| mean_rewards = np.array(rewards).mean() | |
| mean_success_rates = np.array(success_rates).mean() | |
| self.stats_dict["test_rewards"].append(mean_rewards) | |
| self.stats_dict["test_success_rates"].append(mean_success_rates) | |
| self.agent.acmodel.train() | |
| return mean_success_rates, mean_rewards | |
| def load(self): | |
| if os.path.isfile(self.savefile): | |
| with open(self.savefile, 'rb') as f: | |
| stats_dict_loaded = pickle.load(f) | |
| for k, v in stats_dict_loaded.items(): | |
| self.stats_dict[k] = v | |
| else: | |
| raise ValueError(f"Save file {self.savefile} doesn't exist.") | |
| def dump(self): | |
| with open(self.savefile, 'wb') as f: | |
| pickle.dump(self.stats_dict, f) | |