How to train a Deep Q Network¶
Author: PL team
License: CC BY-SA
Generated: 2021-07-26T23:14:45.695289
Main takeaways:
RL has the same flow as previous models we have seen, with a few additions
Handle unsupervised learning by using an IterableDataset where the dataset itself is constantly updated during training
Each training step carries has the agent taking an action in the environment and storing the experience in the IterableDataset
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Setup¶
This notebook requires some packages besides pytorch-lightning.
[1]:
! pip install --quiet "gym" "pytorch-lightning>=1.3" "torch>=1.6, <1.9" "torchmetrics>=0.3"
WARNING: Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/10151>
distutils: /usr/local/lib/python3.9/dist-packages
sysconfig: /usr/lib/python3.9/site-packages
WARNING: Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/10151>
distutils: /usr/local/lib/python3.9/dist-packages
sysconfig: /usr/lib/python3.9/site-packages
WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/10151>
distutils: /usr/local/include/python3.9/UNKNOWN
sysconfig: /usr/include/python3.9/UNKNOWN
WARNING: Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/10151>
distutils: /usr/local/bin
sysconfig: /usr/bin
WARNING: Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/10151>
distutils: /usr/local
sysconfig: /usr
WARNING: Additional context:
user = False
home = None
root = None
prefix = None
[2]:
import os
from collections import deque, namedtuple, OrderedDict
from typing import List, Tuple
import gym
import numpy as np
import torch
from pytorch_lightning import LightningModule, Trainer
from torch import nn, Tensor
from torch.optim import Adam, Optimizer
from torch.utils.data import DataLoader
from torch.utils.data.dataset import IterableDataset
PATH_DATASETS = os.environ.get('PATH_DATASETS', '.')
AVAIL_GPUS = min(1, torch.cuda.device_count())
[3]:
class DQN(nn.Module):
"""
Simple MLP network
"""
def __init__(self, obs_size: int, n_actions: int, hidden_size: int = 128):
"""
Args:
obs_size: observation/state size of the environment
n_actions: number of discrete actions available in the environment
hidden_size: size of hidden layers
"""
super().__init__()
self.net = nn.Sequential(
nn.Linear(obs_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, n_actions),
)
def forward(self, x):
return self.net(x.float())
Memory¶
[4]:
# Named tuple for storing experience steps gathered in training
Experience = namedtuple(
'Experience',
field_names=['state', 'action', 'reward', 'done', 'new_state'],
)
[5]:
class ReplayBuffer:
"""
Replay Buffer for storing past experiences allowing the agent to learn from them
Args:
capacity: size of the buffer
"""
def __init__(self, capacity: int) -> None:
self.buffer = deque(maxlen=capacity)
def __len__(self) -> None:
return len(self.buffer)
def append(self, experience: Experience) -> None:
"""
Add experience to the buffer
Args:
experience: tuple (state, action, reward, done, new_state)
"""
self.buffer.append(experience)
def sample(self, batch_size: int) -> Tuple:
indices = np.random.choice(len(self.buffer), batch_size, replace=False)
states, actions, rewards, dones, next_states = zip(*(self.buffer[idx] for idx in indices))
return (
np.array(states),
np.array(actions),
np.array(rewards, dtype=np.float32),
np.array(dones, dtype=np.bool),
np.array(next_states),
)
[6]:
class RLDataset(IterableDataset):
"""
Iterable Dataset containing the ExperienceBuffer
which will be updated with new experiences during training
Args:
buffer: replay buffer
sample_size: number of experiences to sample at a time
"""
def __init__(self, buffer: ReplayBuffer, sample_size: int = 200) -> None:
self.buffer = buffer
self.sample_size = sample_size
def __iter__(self) -> Tuple:
states, actions, rewards, dones, new_states = self.buffer.sample(self.sample_size)
for i in range(len(dones)):
yield states[i], actions[i], rewards[i], dones[i], new_states[i]
Agent¶
[7]:
class Agent:
"""
Base Agent class handeling the interaction with the environment
"""
def __init__(self, env: gym.Env, replay_buffer: ReplayBuffer) -> None:
"""
Args:
env: training environment
replay_buffer: replay buffer storing experiences
"""
self.env = env
self.replay_buffer = replay_buffer
self.reset()
self.state = self.env.reset()
def reset(self) -> None:
""" Resents the environment and updates the state"""
self.state = self.env.reset()
def get_action(self, net: nn.Module, epsilon: float, device: str) -> int:
"""Using the given network, decide what action to carry out
using an epsilon-greedy policy
Args:
net: DQN network
epsilon: value to determine likelihood of taking a random action
device: current device
Returns:
action
"""
if np.random.random() < epsilon:
action = self.env.action_space.sample()
else:
state = torch.tensor([self.state])
if device not in ['cpu']:
state = state.cuda(device)
q_values = net(state)
_, action = torch.max(q_values, dim=1)
action = int(action.item())
return action
@torch.no_grad()
def play_step(
self,
net: nn.Module,
epsilon: float = 0.0,
device: str = 'cpu',
) -> Tuple[float, bool]:
"""Carries out a single interaction step between the agent and the environment
Args:
net: DQN network
epsilon: value to determine likelihood of taking a random action
device: current device
Returns:
reward, done
"""
action = self.get_action(net, epsilon, device)
# do step in the environment
new_state, reward, done, _ = self.env.step(action)
exp = Experience(self.state, action, reward, done, new_state)
self.replay_buffer.append(exp)
self.state = new_state
if done:
self.reset()
return reward, done
DQN Lightning Module¶
[8]:
class DQNLightning(LightningModule):
""" Basic DQN Model """
def __init__(
self,
batch_size: int = 16,
lr: float = 1e-2,
env: str = "CartPole-v0",
gamma: float = 0.99,
sync_rate: int = 10,
replay_size: int = 1000,
warm_start_size: int = 1000,
eps_last_frame: int = 1000,
eps_start: float = 1.0,
eps_end: float = 0.01,
episode_length: int = 200,
warm_start_steps: int = 1000,
) -> None:
"""
Args:
batch_size: size of the batches")
lr: learning rate
env: gym environment tag
gamma: discount factor
sync_rate: how many frames do we update the target network
replay_size: capacity of the replay buffer
warm_start_size: how many samples do we use to fill our buffer at the start of training
eps_last_frame: what frame should epsilon stop decaying
eps_start: starting value of epsilon
eps_end: final value of epsilon
episode_length: max length of an episode
warm_start_steps: max episode reward in the environment
"""
super().__init__()
self.save_hyperparameters()
self.env = gym.make(self.hparams.env)
obs_size = self.env.observation_space.shape[0]
n_actions = self.env.action_space.n
self.net = DQN(obs_size, n_actions)
self.target_net = DQN(obs_size, n_actions)
self.buffer = ReplayBuffer(self.hparams.replay_size)
self.agent = Agent(self.env, self.buffer)
self.total_reward = 0
self.episode_reward = 0
self.populate(self.hparams.warm_start_steps)
def populate(self, steps: int = 1000) -> None:
"""
Carries out several random steps through the environment to initially fill
up the replay buffer with experiences
Args:
steps: number of random steps to populate the buffer with
"""
for i in range(steps):
self.agent.play_step(self.net, epsilon=1.0)
def forward(self, x: Tensor) -> Tensor:
"""
Passes in a state x through the network and gets the q_values of each action as an output
Args:
x: environment state
Returns:
q values
"""
output = self.net(x)
return output
def dqn_mse_loss(self, batch: Tuple[Tensor, Tensor]) -> Tensor:
"""
Calculates the mse loss using a mini batch from the replay buffer
Args:
batch: current mini batch of replay data
Returns:
loss
"""
states, actions, rewards, dones, next_states = batch
state_action_values = self.net(states).gather(1, actions.unsqueeze(-1)).squeeze(-1)
with torch.no_grad():
next_state_values = self.target_net(next_states).max(1)[0]
next_state_values[dones] = 0.0
next_state_values = next_state_values.detach()
expected_state_action_values = next_state_values * self.hparams.gamma + rewards
return nn.MSELoss()(state_action_values, expected_state_action_values)
def training_step(self, batch: Tuple[Tensor, Tensor], nb_batch) -> OrderedDict:
"""
Carries out a single step through the environment to update the replay buffer.
Then calculates loss based on the minibatch recieved
Args:
batch: current mini batch of replay data
nb_batch: batch number
Returns:
Training loss and log metrics
"""
device = self.get_device(batch)
epsilon = max(
self.hparams.eps_end,
self.hparams.eps_start - self.global_step + 1 / self.hparams.eps_last_frame,
)
# step through environment with agent
reward, done = self.agent.play_step(self.net, epsilon, device)
self.episode_reward += reward
# calculates training loss
loss = self.dqn_mse_loss(batch)
if self.trainer.use_dp or self.trainer.use_ddp2:
loss = loss.unsqueeze(0)
if done:
self.total_reward = self.episode_reward
self.episode_reward = 0
# Soft update of target network
if self.global_step % self.hparams.sync_rate == 0:
self.target_net.load_state_dict(self.net.state_dict())
log = {
'total_reward': torch.tensor(self.total_reward).to(device),
'reward': torch.tensor(reward).to(device),
'train_loss': loss
}
status = {
'steps': torch.tensor(self.global_step).to(device),
'total_reward': torch.tensor(self.total_reward).to(device)
}
return OrderedDict({'loss': loss, 'log': log, 'progress_bar': status})
def configure_optimizers(self) -> List[Optimizer]:
""" Initialize Adam optimizer"""
optimizer = Adam(self.net.parameters(), lr=self.hparams.lr)
return [optimizer]
def __dataloader(self) -> DataLoader:
"""Initialize the Replay Buffer dataset used for retrieving experiences"""
dataset = RLDataset(self.buffer, self.hparams.episode_length)
dataloader = DataLoader(
dataset=dataset,
batch_size=self.hparams.batch_size,
)
return dataloader
def train_dataloader(self) -> DataLoader:
"""Get train loader"""
return self.__dataloader()
def get_device(self, batch) -> str:
"""Retrieve device currently being used by minibatch"""
return batch[0].device.index if self.on_gpu else 'cpu'
Trainer¶
[9]:
model = DQNLightning()
trainer = Trainer(
gpus=AVAIL_GPUS,
max_epochs=200,
val_check_interval=100,
)
trainer.fit(model)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
| Name | Type | Params
------------------------------------
0 | net | DQN | 898
1 | target_net | DQN | 898
------------------------------------
1.8 K Trainable params
0 Non-trainable params
1.8 K Total params
0.007 Total estimated model params size (MB)
/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:102: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
/tmp/ipykernel_728/3502201211.py:32: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
np.array(dones, dtype=np.bool),
/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/deprecated_api.py:70: LightningDeprecationWarning: Internal: `use_dp` is deprecated in v1.2 and will be removed in v1.4.
rank_zero_deprecation("Internal: `use_dp` is deprecated in v1.2 and will be removed in v1.4.")
/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/deprecated_api.py:92: LightningDeprecationWarning: Internal: `use_ddp2` is deprecated in v1.2 and will be removed in v1.4.
rank_zero_deprecation("Internal: `use_ddp2` is deprecated in v1.2 and will be removed in v1.4.")
[10]:
# Start tensorboard.
%load_ext tensorboard
%tensorboard --logdir lightning_logs/
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