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How to use start_replay method in localstack

Best Python code snippet using localstack_python

train.py

Source:train.py

1import numpy as np2import argparse3from cave import DQNCave4from dqn_agent import DQNAgent5from drqn_agent import DRQNAgent6import copy7from collections import deque8if __name__ == "__main__":9    parser = argparse.ArgumentParser()10    parser.add_argument("-m", "--model_path")11    parser.add_argument("-l", "--load", dest="load", action="store_true", default=False)12    parser.add_argument("-e", "--epoch-num", dest="n_epochs", default=200, type=int)13    parser.add_argument("-s", "--save-interval", dest="save_interval", default=1000, type=int)14    parser.add_argument("-g", "--graves", dest="graves", action="store_true", default=False, help='Use RmpropGraves (default: off)')15    parser.add_argument("-r", "--recurent", dest="drqn", action="store_true", default=False, help='Use DRQN (default: off)')16    parser.add_argument("-d", "--ddqn", dest="ddqn", action="store_true", default=False, help='Use Double DQN (default: off)')17    args = parser.parse_args()18    # parameters19    n_epochs = args.n_epochs20    state_num = 521    # environment, agent22    env = DQNCave()23    Agent = DRQNAgent if args.drqn else DQNAgent24    agent = Agent(env.enable_actions, env.name, env.size, state_num, graves=args.graves, ddqn=args.ddqn)25    agent.init_model()26    if args.load:27        agent.load_model(args.model_path)28    # variables29    e = 030    total_frame = 031    while e < n_epochs:32        # reset33        frame = 034        loss = 0.035        do_replay_count = 036        Q_max = 0.037        env.reset()38        state_t_1, reward_t, terminal, past_time = env.observe()39        S = deque(maxlen=state_num * 2)40        while not terminal:41            state_t = state_t_142            if len(S) == 0:43                [S.append([state_t] if args.drqn else state_t) for i in range(state_num * 2)]44            else:45                S.append([state_t] if args.drqn else state_t)46                # execute action in environment47            tmpS = [S[(s + 1) * 2 - 1] for s in range(state_num)]48            if frame % 3 == 0:49                action_t = agent.select_action(tmpS, agent.exploration)50                #action_t = agent.select_action([state_t], agent.exploration)51            env.execute_action(action_t)52            # observe environment53            state_t_1, reward_t, terminal, past_time = env.observe()54            # store experience55            start_replay = False56            if frame % 4 == 0 or reward_t == -1:57                new_S = copy.copy(S)58                new_S.append([state_t_1] if args.drqn else state_t_1)59                tmpnew_S = [new_S[(s + 1) * 2 - 1] for s in range(state_num)]60                score = past_time if e > 200 else None61                start_replay = agent.store_experience(tmpS, action_t, reward_t, tmpnew_S, terminal, score)62                #start_replay = agent.store_experience([state_t], action_t, reward_t, [state_t_1], terminal)63            # experience replay64            if start_replay:65                do_replay_count += 166                if do_replay_count % 3 == 0 or reward_t == -1:67                    agent.update_exploration(e)68                    agent.experience_replay()69                    do_replay_count = 070            # update target network71            if total_frame % 5000 == 0 and start_replay:72                agent.update_target_model()73            # for log74            frame += 175            total_frame += 176            loss += agent.current_loss77            Q_max += np.max(agent.Q_values(tmpS))78        if start_replay:79            print("EPOCH: {:03d}/{:03d} | SCORE: {:03d} | LOSS: {:.4f} | Q_MAX: {:.4f}".format(80                e, n_epochs - 1, past_time, loss / frame, Q_max / frame))81        if e > 0 and e % args.save_interval == 0:82            agent.save_model(e)83            agent.save_model()84        if start_replay:85            e += 186    # save model...

patch_socket.py

Source:patch_socket.py

...9    def start_record(self):10        self.recording = True11        self.replaying = False12        self.records = []13    def start_replay(self):14        self.recording = False15        self.replaying = True16        self.replay_records = self.records[:]17    def settimeout(self, n):18        return self._ss.settimeout(n)19    def connect(self, *args, **kwargs):20        return self._ss.connect(*args, **kwargs)21    def setsockopt(self, *args):22        if not self.replaying:23            return self._ss.setsockopt(*args)24    def shutdown(self, *args):25        if not self.replaying:26            return self._ss.shutdown(*args)27    def send(self, buf):28        if self.replaying:29            return len(buf)30        else:31            return self._ss.send(buf)32    def sendall(self, buf):33        if self.replaying:34            return len(buf)35        else:36            return self._ss.sendall(buf)37    def recv(self, size):38        if self.replaying:39            return self.replay_records.pop(0)40        buf = self._ss.recv(size)41        if self.recording:42            self.records.append(buf)43        return buf44    def recv_into(self, buf):45        if self.replaying:46            s = self.replay_records.pop(0)47            buf[:len(s)] = s48            return len(s)49        ret = self._ss.recv_into(buf)50        if self.recording:51            self.records.append(buf[:ret])52        return ret53    def close(self):54        return self._ss.close()55import socket as socketmodule56socketmodule.socket = socket57def start_record(socks):58    for sock in socks:59        sock.start_record()60def start_replay(socks):61    for sock in socks:62        sock.start_replay()63def run_with_recording(socks, func):64    for sock in socks:65        sock.start_record()66    func()67def run_with_replay(socks, func):68    for sock in socks:69        sock.start_replay()...

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