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test_w_noise.py
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test_w_noise.py
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import os
import yaml
import pickle
import argparse
import datetime
import scipy.signal
import numpy as np
import torch as T
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from collections import namedtuple
from models.a2c_dnd_lstm import A2C_DND_LSTM
from tasks.ep_two_step import EpTwoStepTask
Rollout = namedtuple('Rollout',
('state', 'action', 'reward', 'timestep', 'done', 'policy', 'value'))
class Trainer:
def __init__(self, config, noise_idx = None):
self.device = 'cpu'
self.seed = config["seed"]
self.mode = config["mode"]
T.manual_seed(config["seed"])
np.random.seed(config["seed"])
T.random.manual_seed(config["seed"])
self.env = EpTwoStepTask(config["task"])
self.agent = A2C_DND_LSTM(
self.env.feat_size,
config["agent"]["mem-units"],
self.env.num_actions,
config["agent"]["dict-len"],
config["agent"]["dict-kernel"],
noise_idx
).to(self.device)
self.optim = T.optim.RMSprop(self.agent.parameters(), lr=config["agent"]["lr"])
self.val_coeff = config["agent"]["value-loss-weight"]
self.entropy_coeff = config["agent"]["entropy-weight"]
self.max_grad_norm = config["agent"]["max-grad-norm"]
self.switch_p = config["task"]["swtich-prob"]
self.start_episode = 0
self.writer = SummaryWriter(log_dir=os.path.join("logs_ep", config["run-title"]))
self.save_path = os.path.join(config["save-path"], config["run-title"], config["run-title"]+"_{epi:04d}")
print("> Loading Checkpoint")
self.start_episode = config["start-episode"]
self.agent.load_state_dict(T.load(self.save_path.format(epi=self.start_episode) + ".pt")["state_dict"])
def run_episode(self, episode):
done = False
total_reward = 0
p_action, p_reward, timestep = [0,0], 0, 0
self.agent.reset_memory()
self.agent.turn_on_encoding()
state = self.env.reset()
(h_tm1, c_tm1) = self.agent.get_init_states()
buffer = []
while not done:
# switch reward contingencies at the beginning of each episode with probability p
self.env.possible_switch(switch_p=self.switch_p)
if self.env.trial == "cued" and self.mode == "episodic":
self.agent.turn_on_retrieval()
else:
self.agent.turn_off_retrieval()
cue = self.env.get_cue()
cue = T.tensor(cue, device=self.device)
# sample action using model
x_t = (
T.tensor([state], device=self.device).float(),
T.tensor([p_action], device=self.device).float(),
T.tensor([[p_reward]], device=self.device).float(),
T.tensor([[timestep]], device=self.device).float(),
)
action_dist, values, (h_t, c_t) = self.agent(x_t, cue, (h_tm1, c_tm1))
action_cat = T.distributions.Categorical(action_dist.squeeze())
action = action_cat.sample()
action_onehot = np.eye(2)[action]
# take action and observe result
new_state, reward, done, timestep, context = self.env.step(int(action), cue.cpu().numpy())
context = T.tensor(context, device=self.device)
self.agent.save_memory(context, c_t)
# ('state', 'action', 'reward', 'timestep', 'done', 'policy', 'value')
buffer += [Rollout(
state,
action_onehot,
reward,
timestep,
done,
action_dist,
values
)]
state = new_state
p_reward = reward
p_action = action_onehot
c_tm1 = c_t
h_tm1 = h_t
total_reward += reward
# boostrap final observation
cue = self.env.get_cue()
cue = T.tensor(cue, device=self.device)
_, values, _ = self.agent((
T.tensor([state], device=self.device).float(),
T.tensor([p_action], device=self.device).float(),
T.tensor([[p_reward]], device=self.device).float(),
T.tensor([[timestep]], device=self.device).float(),
), cue, (h_t, c_t))
buffer += [Rollout(None, None, None, None, None, None, values)]
return total_reward, buffer
def test(self, num_episodes):
progress = tqdm(range(num_episodes))
self.env.reset_transition_count()
self.agent.eval()
total_rewards = np.zeros(num_episodes)
rt_list = []
for episode in progress:
reward, _ = self.run_episode(episode)
rt, _, _ = self.agent.get_gates()
self.agent.ep_lstm.reset_gate_monitor()
rt_list += [rt]
total_rewards[episode] = reward
avg_reward = total_rewards[max(0, episode-10):(episode+1)].mean()
progress.set_description(f"Episode {episode}/{num_episodes} | Reward: {reward} | Last 10: {avg_reward:.4f}")
if self.mode == "incremental":
self.env.plot(self.save_path.format(epi=self.seed) + "_uncued", self.env.transition_count_uncued, "Incremental Uncued", y_lim=0)
self.env.plot(self.save_path.format(epi=self.seed) + "_cued", self.env.transition_count_cued, "Incremental Cued", y_lim=0)
elif self.mode == "episodic":
self.env.plot(self.save_path.format(epi=self.seed) + "_episodic", self.env.transition_count_episodic, "Episodic", y_lim=0)
return rt_list, self.env.total_reward_cued / (num_episodes*50), self.env.total_reward_uncued / (num_episodes*50)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Paramaters')
parser.add_argument('-c', '--config', type=str, default="configs/ep_two_step.yaml", help='path of config file')
args = parser.parse_args()
with open(args.config, 'r', encoding="utf-8") as fin:
config = yaml.load(fin, Loader=yaml.FullLoader)
n_seeds = config["n-seeds"]
base_seed = config["seed"]
base_run_title = config["run-title"]
threshold = 0.2
reward_cued = np.zeros(n_seeds)
reward_uncued = np.zeros(n_seeds)
for seed_idx in range(1, n_seeds + 1):
config["run-title"] = base_run_title + f"_{seed_idx}"
config["seed"] = base_seed * seed_idx
exp_path = os.path.join(config["save-path"], config["run-title"])
if not os.path.isdir(exp_path):
os.makedirs(exp_path)
out_path = os.path.join(exp_path, os.path.basename(args.config))
with open(out_path, 'w') as fout:
yaml.dump(config, fout)
print(f"> Running {config['run-title']}")
trainer = Trainer(config)
rt, _, _ = trainer.test(config["task"]["test-episodes"])
rt = np.array(rt).mean(axis=0)
rt_select = np.arange(config["agent"]["mem-units"])[rt >= 1-threshold]
print(f"{len(rt_select)}/{len(rt)}")
# trainer = Trainer(config, noise_idx=rt_select)
# _, reward_cued[seed_idx-1], reward_uncued[seed_idx-1] = trainer.test(config["task"]["test-episodes"])
save_path = os.path.join(config["save-path"], "reward_cued.npy")
np.save(save_path, reward_cued)
save_path = os.path.join(config["save-path"], "reward_uncued.npy")
np.save(save_path, reward_uncued)