Reinforcement-Learning-With-Python/06_dqn_dueling.py at master · Prakhar-FF13/Reinforcement-Learning-With-Python · GitHub

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#!/usr/bin/env python3
import gym
import ptan
import argparse
import numpy as np

import torch
import torch.nn as nn
import torch.optim as optim

from tensorboardX import SummaryWriter

from lib2 import common


class DuelingDQN(nn.Module):
    def __init__(self, input_shape, n_actions):
        super(DuelingDQN, self).__init__()

        self.conv = nn.Sequential(
            nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.ReLU()
        )

        conv_out_size = self._get_conv_out(input_shape)
        self.fc_adv = nn.Sequential(
            nn.Linear(conv_out_size, 512),
            nn.ReLU(),
            nn.Linear(512, n_actions)
        )
        self.fc_val = nn.Sequential(
            nn.Linear(conv_out_size, 512),
            nn.ReLU(),
            nn.Linear(512, 1)
        )

    def _get_conv_out(self, shape):
        o = self.conv(torch.zeros(1, *shape))
        return int(np.prod(o.size()))

    def forward(self, x):
        fx = x.float() / 256
        conv_out = self.conv(fx).view(fx.size()[0], -1)
        val = self.fc_val(conv_out)
        adv = self.fc_adv(conv_out)
        return val + adv - adv.mean()


if __name__ == "__main__":
    params = common.HYPERPARAMS['pong']
    parser = argparse.ArgumentParser()
    parser.add_argument("--cuda", default=False, action="store_true", help="Enable cuda")
    args = parser.parse_args()
    device = torch.device("cuda" if args.cuda else "cpu")

    env = gym.make(params['env_name'])
    env = ptan.common.wrappers.wrap_dqn(env)

    writer = SummaryWriter(comment="-" + params['run_name'] + "-dueling")
    net = DuelingDQN(env.observation_space.shape, env.action_space.n).to(device)
    tgt_net = ptan.agent.TargetNet(net)
    selector = ptan.actions.EpsilonGreedyActionSelector(epsilon=params['epsilon_start'])
    epsilon_tracker = common.EpsilonTracker(selector, params)
    agent = ptan.agent.DQNAgent(net, selector, device=device)

    exp_source = ptan.experience.ExperienceSourceFirstLast(env, agent, gamma=params['gamma'], steps_count=1)
    buffer = ptan.experience.ExperienceReplayBuffer(exp_source, buffer_size=params['replay_size'])
    optimizer = optim.Adam(net.parameters(), lr=params['learning_rate'])

    frame_idx = 0

    with common.RewardTracker(writer, params['stop_reward']) as reward_tracker:
        while True:
            frame_idx += 1
            buffer.populate(1)
            epsilon_tracker.frame(frame_idx)

            new_rewards = exp_source.pop_total_rewards()
            if new_rewards:
                if reward_tracker.reward(new_rewards[0], frame_idx, selector.epsilon):
                    break

            if len(buffer) < params['replay_initial']:
                continue

            optimizer.zero_grad()
            batch = buffer.sample(params['batch_size'])
            loss_v = common.calc_loss_dqn(batch, net, tgt_net.target_model, gamma=params['gamma'], device=device)
            loss_v.backward()
            optimizer.step()

            if frame_idx % params['target_net_sync'] == 0:
                tgt_net.sync()