Overview of TD3 (Twin Delayed Deep Deterministic Policy Gradient)
TD3 (Twin Delayed Deep Deterministic Policy Gradient) is a type of Actor-Critic method, as described in “Actor-Critic Overview, Algorithm and Implementation Examples” in the continuous action space in reinforcement learning. TD3 is a type of Actor-Critic method, as described in “Overview of Deep Deterministic Policy Gradient (DDPG) and Examples of Algorithms and Implementations“. Policy Gradient (DDPG) algorithm described in “Deep Deterministic Policy Gradient (DDPG) Overview, Algorithm and Example Implementations” and aims at more stable learning and improved performance.
An overview of TD3 is as follows.
1. extension of the actor-critique method: TD3 is a type of actor-critique method that combines two neural networks, actor (policy) and critique (value function), where the actor network approximates the policy and the critique network approximates the state value function.
2. twin critics: TD3 uses two critique networks, which allows for a more stable value function to be learnt; by selecting the minimum value from the outputs of the two critique networks to update the value function, the effect of noise and bias is reduced.
3. delayed update: TD3 improves the stability of learning by delaying the updates of actors and clitics. Specifically, the actor’s policy updates are delayed and the performance of the actor’s policy is evaluated and updated while the value function of the critique is updated.
4. adding noise to the target policy: TD3 promotes more exploratory learning by adding noise when updating the target policy. This reduces the risk of convergence to a locally optimal solution and allows a wider policy space to be explored.
TD3 is a method aimed at improving the performance and learning stability of DDPG, and is a method that has been reported to perform well in reinforcement learning problems in continuous action spaces.
Algorithms associated with TD3 (Twin Delayed Deep Deterministic Policy Gradient).
The basic steps of the TD3 algorithm are given below.
1. initialisation of the Actor Network and Critic Network: the Actor Network is used to approximate the measures and the Critic Network is used to approximate the state value function In TD3, two clitic networks (main clitic and sub-clitic) are used.
2. initialisation of the target network: the target network is used to track the parameters of the actor network and the critical network as targets.
3. data collection from the environment: the agent interacts with the environment, selects actions and observes the next state and immediate rewards.
4. calculating TD errors from twin critiques: two TD errors are calculated from the twin critique network. Select the minimum of these errors to obtain the final TD error.
5. updating the actor network: the actor network is updated using the TD errors calculated from the clitic network.
6. updating the clitic network: the clitic network is updated to minimise the TD error.
7. updating the target network: the target network is gradually brought closer to the actor network and the critical network using the Soft Update or policy target methods.
8. assessing convergence: the above steps are repeated until learning converges or certain criteria are achieved.
TD3 is an extension of DDPG, which is an algorithm that aims to improve the stability and performance of learning by introducing features such as twin critics, delayed updates and noise addition.
TD3 (Twin Delayed Deep Deterministic Policy Gradient) application examples
The following are examples of the application of TD3.
1. robot control: TD3 has been applied to robot control problems. For example, it is used to learn tasks such as manipulating and moving a robot arm, where TD3 allows the robot to select a sequence of actions and learn from its interactions with the environment.
2. automated driving: TD3 has also been applied to automated driving problems. Automated vehicles need to operate safely and efficiently in a variety of situations and TD3 can improve the ability of automated vehicles to adapt to complex traffic situations and select appropriate behaviour.
3. finance: TD3 has also been applied to optimise financial transactions. Financial markets are complex and uncertain environments and TD3 can be used to learn trading strategies to maximise returns.
4. gaming: TD3 has also been applied to game-playing problems such as video games and board games, where TD3 can be used to enable game agents to learn optimal behavioural strategies and achieve high performance.
TD3 has been reported to be very effective and perform well for reinforcement learning problems in continuous action spaces.
Example implementation of TD3 (Twin Delayed Deep Deterministic Policy Gradient)
The following is a simple example of implementing the TD3 algorithm using PyTorch. In this example, TD3 is used to solve the reinforcement learning problem in a continuous action space to train agents in a continuous action space.
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import random
import copy
# TD3 algorithm implementation.
class TD3:
def __init__(self, state_dim, action_dim, max_action):
# Initialisation of neural networks
self.actor = Actor(state_dim, action_dim, max_action)
self.actor_target = copy.deepcopy(self.actor)
self.actor_optimizer = optim.Adam(self.actor.parameters())
self.critic = Critic(state_dim, action_dim)
self.critic_target = copy.deepcopy(self.critic)
self.critic_optimizer = optim.Adam(self.critic.parameters())
self.max_action = max_action
def select_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1))
action = self.actor(state).cpu().data.numpy().flatten()
return action
def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2):
for it in range(iterations):
# Randomly sampling batches from the replay buffer.
batch_states, batch_next_states, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(batch_size)
state = torch.FloatTensor(batch_states)
next_state = torch.FloatTensor(batch_next_states)
action = torch.FloatTensor(batch_actions)
reward = torch.FloatTensor(batch_rewards)
done = torch.FloatTensor(batch_dones)
# Critique update
next_action = self.actor_target(next_state)
noise = torch.normal(0, policy_noise, size=next_action.size())
noise = torch.clamp(noise, -noise_clip, noise_clip)
next_action += noise
next_action = torch.clamp(next_action, -self.max_action, self.max_action)
target_Q1, target_Q2 = self.critic_target(next_state, next_action)
target_Q = torch.min(target_Q1, target_Q2)
target_Q = reward + ((1 - done) * discount * target_Q).detach()
current_Q1, current_Q2 = self.critic(state, action)
critic_loss = nn.MSELoss()(current_Q1, target_Q) + nn.MSELoss()(current_Q2, target_Q)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# Actor updates.
if it % policy_freq == 0:
actor_loss = -self.critic.Q1(state, self.actor(state)).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# software update
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
# Definition of actor networks.
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.layer1 = nn.Linear(state_dim, 400)
self.layer2 = nn.Linear(400, 300)
self.layer3 = nn.Linear(300, action_dim)
self.max_action = max_action
def forward(self, x):
x = torch.relu(self.layer1(x))
x = torch.relu(self.layer2(x))
x = self.max_action * torch.tanh(self.layer3(x))
return x
# Definition of a clitic network.
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.layer1 = nn.Linear(state_dim + action_dim, 400)
self.layer2 = nn.Linear(400, 300)
self.layer3 = nn.Linear(300, 1)
def forward(self, x, u):
x = torch.relu(self.layer1(torch.cat([x, u], 1)))
x = torch.relu(self.layer2(x))
x = self.layer3(x)
return x
# Definition of replay buffer
class ReplayBuffer:
def __init__(self, max_size=1000000):
self.storage = []
self.max_size = max_size
self.ptr = 0
def add(self, state, next_state, action, reward, done):
if len(self.storage) == self.max_size:
self.storage[int(self.ptr)] = (state, next_state, action, reward, done)
self.ptr = (self.ptr + 1) % self.max_size
else:
self.storage.append((state, next_state, action, reward, done))
def sample(self, batch_size):
ind = np.random.randint(0, len(self.storage), size=batch_size)
states, next_states, actions, rewards, dones = [], [], [], [], []
for i in ind:
s, s_, a, r, d = self.storage[i]
states.append(np.array(s, copy=False))
next_states.append(np.array(s_, copy=False))
actions.append(np.array(a, copy=False))
rewards.append(np.array(r, copy=False))
dones.append(np.array(d, copy=False))
return np.array(states), np.array(next_states), np.array(actions), np.array(rewardsTD3 (Twin Delayed Deep Deterministic Policy Gradient) challenges and measures to address them.
While TD3 (Twin Delayed Deep Deterministic Policy Gradient) offers high performance, it may face some challenges. These challenges and their countermeasures are described below.
1. Excessive policy updates: the frequent policy updates of actors in TD3 may lead to unstable learning. In particular, depending on the environment and the problem, excessive policy updates can cause performance degradation and learning stagnation.
Solution: adjusting the frequency of policy updates: adjusting the frequency of policy updates can improve learning stability, while reducing the frequency of updates can prevent excessive policy updates.
2. difficulty in adjusting hyper-parameters: there are many hyper-parameters in TD3 and adjusting these hyper-parameters is important for successful learning. In particular, parameters such as learning rate and discount rate affect learning convergence and performance.
Solution: tuning of hyperparameters: tuning of hyperparameters can improve the convergence and performance of learning. Tuning of hyperparameters should be based on experiments and experience.
3. convergence to a local optimum: TD3 may converge to a local optimum, especially during the initialisation and learning process, which increases the risk of convergence to a local optimum.
Solution: learning from a variety of initial values: learning can be started from several different initial values to reduce the risk of convergence to the local optimum solution. Use random or heuristic initialisation to ensure a diversity of initial values.
References and Reference Books
Details of reinforcement learning are described in “Theories and Algorithms of Various Reinforcement Learning Techniques and Their Python Implementations. Please also refer to this page.
A reference book is “Reinforcement Learning: An Introduction, Second Edition.
“Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym“
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