Multi-agent systems with deep reinforcement learning (DRL).
There are several methods for implementing multi-agent systems using deep reinforcement learning (DRL). The general methods are described below.
1. defining the environment: define the environment of the multi-agent system. This will be the place where multiple agents interact, where they can act, observe states and receive rewards. Examples could include a multi-player online game or a traffic simulation.
2. agent definition: for each agent in the system, a separate agent is defined. Each agent contains sensors to observe states, policies to select behaviours and learning algorithms to control interactions with the environment.
3. defining shared states and behaviours: if there are states and behaviours shared by agents in the system, define these. For example, if multiple agents compete for use of the same resource, the state of that resource and available behaviours need to be defined.
4. communication and co-operation: multi-agent systems require agents to communicate and co-operate with each other to accomplish tasks. This includes the implementation of messaging protocols and co-operation algorithms.
5. selection of learning algorithms: in multi-agent systems, each agent needs to learn from its interactions with the environment. This will typically involve the use of learning algorithms such as Q-learning, actor-critic methods and multi-agent DQN.
6. training and evaluation: training of multi-agent systems involves learning through interactions between agents. After training, it is important to evaluate the performance of the system and make improvements where necessary.
The implementation of multi-agent systems involves complex elements such as agent interaction, co-operation and competition, which need to be thoroughly considered and tested, and attention should also be paid to the stability and convergence of the training process when using DRLs.
Algorithms used in multi-agent systems with deep reinforcement learning (DRL).
There is a wide range of algorithms for using deep reinforcement learning (DRL) in multi-agent systems. Some common multi-agent DRL algorithms are described below.
1. Multi-Agent Deep Deterministic Policy Gradient (MADDPG): MADDPG is described in “Overview of Deep Deterministic Policy Gradient (DDPG) and examples of algorithms and implementations“. Deep Deterministic Policy Gradient (DDPG) is an extension of the single-agent DRL algorithm Deep Deterministic Policy Gradient (DDPG) to multi-agents as described in “Overview of Deep Deterministic Policy Gradient (DDPG) and examples of algorithms and implementations”. Each agent has its own behaviour policy and learns by observing the behaviour of other agents; MADDPG is suitable for scenarios where agents share a common environment and interact with each other with individual policies.
2. Multi-Agent Proximal Policy Optimisation (MAPPO): MAPPO is a multi-agent version of the Proximal Policy Optimisation (PPO) algorithm described in “Overview of Proximal Policy Optimisation (PPO) and examples of algorithms and implementations“. MAPPO improves learning stability by constraining the proximity of each agent to the policies of other agents when it updates its own policy.
3.Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments (MAAC): MAAC is a multi-agent type of Actor-Critic method (Actor-Critic), also described in the multi-agent “A2C (Advantage Actor-Critic) Overview and Examples of Algorithms and Implementations“, which is suitable when agents interact in mixed cooperative and competitive environments. Learning.
4. Decentralised Distributed Proximal Policy Optimisation (DDPPO): DDPPO is an extension of the multi-agent PPO algorithm to distributed environments. Each agent learns independently in a distributed environment and updates its policy by aggregating the collected data to a central learning node.
Application of deep reinforcement learning (DRL) to multi-agent systems.
The following are examples of applications of deep reinforcement learning (DRL) in multi-agent systems.
1. multiplayer games: DRL has been used to learn strategies and optimise the behaviour of multiplayer games in which multiple agents interact. For example, multi-agent DRL has been used in real-time strategy and multiplayer online battle arena (MOBA) games such as StarCraft II and DotA2 (Dota 2).
2. traffic control systems: DRL is used to optimise signal control and intersection control in traffic control systems. The aim is to optimise traffic flows with multiple traffic agents acting simultaneously.
3. robot simulation: multi-agent DRL is applied in scenarios where multiple robots co-operate to accomplish tasks. Examples include the optimisation of logistics tasks and co-operative work in warehouses.
4. agent interaction in an organisation: multi-agent DRL has been applied in scenarios where multiple agents (e.g. humans and software agents) in an organisation interact. Examples include production processes, team cooperation and asset management.
In these applications, multiple agents need to achieve goals through cooperation or competition, and multi-agent DRL is effective in learning and optimising behaviour in such complex environments and may provide new solutions to real-world problems.
Example implementation of a multi-agent system using deep reinforcement learning (DRL)
An example implementation of a multi-agent system using deep reinforcement learning (DRL) is presented. The implementation of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is shown here using the PyTorch library.
First, the required libraries are imported.
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as FNext, a neural network for multi-agents is defined.
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim):
super(Actor, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, action_dim)
def forward(self, state):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
x = torch.tanh(self.fc3(x))
return x
class Critic(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, 1)
def forward(self, state, action):
x = torch.cat([state, action], 1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return xThe next step is to implement the MADDPG algorithm.
class MADDPG:
def __init__(self, num_agents, state_dim, action_dim, hidden_dim, lr_actor=1e-3, lr_critic=1e-3, gamma=0.99, tau=0.01):
self.num_agents = num_agents
self.actors = [Actor(state_dim, action_dim, hidden_dim) for _ in range(num_agents)]
self.target_actors = [Actor(state_dim, action_dim, hidden_dim) for _ in range(num_agents)]
self.critics = [Critic(state_dim*num_agents, action_dim*num_agents, hidden_dim) for _ in range(num_agents)]
self.target_critics = [Critic(state_dim*num_agents, action_dim*num_agents, hidden_dim) for _ in range(num_agents)]
self.actor_optimizers = [optim.Adam(actor.parameters(), lr=lr_actor) for actor in self.actors]
self.critic_optimizers = [optim.Adam(critic.parameters(), lr=lr_critic) for critic in self.critics]
self.gamma = gamma
self.tau = tau
for i in range(num_agents):
self.target_actors[i].load_state_dict(self.actors[i].state_dict())
self.target_critics[i].load_state_dict(self.critics[i].state_dict())
def select_action(self, states):
actions = [actor(torch.tensor(states[i]).float()) for i, actor in enumerate(self.actors)]
return torch.stack(actions)
def update(self, states, actions, rewards, next_states, dones):
for i in range(self.num_agents):
state = torch.tensor(states[i]).float()
action = torch.tensor(actions[i]).float()
reward = torch.tensor(rewards[i]).float()
next_state = torch.tensor(next_states[i]).float()
done = torch.tensor(dones[i]).float()
# Update critic
target_actions = torch.stack([self.target_actors[j](next_state) for j in range(self.num_agents)])
target_q = torch.squeeze(self.target_critics[i](torch.cat([next_state] + target_actions, 1)))
target_value = reward + self.gamma * (1 - done) * target_q.detach()
value = torch.squeeze(self.critics[i](torch.cat([state, action], 1)))
critic_loss = F.mse_loss(value, target_value)
self.critic_optimizers[i].zero_grad()
critic_loss.backward()
self.critic_optimizers[i].step()
# Update actor
policy_loss = -self.critics[i](torch.cat([state, self.actors[i](state)], 1)).mean()
self.actor_optimizers[i].zero_grad()
policy_loss.backward()
self.actor_optimizers[i].step()
# Update target networks
for param, target_param in zip(self.actors[i].parameters(), self.target_actors[i].parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
for param, target_param in zip(self.critics[i].parameters(), self.target_critics[i].parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)Challenges and countermeasures for multi-agent systems using deep reinforcement learning (DRL).
This section describes challenges and countermeasures for multi-agent systems with deep reinforcement learning (DRL).
1. non-cooperative learning and conflict: in multi-agent systems, agents may compete with each other or need to cooperate. In non-cooperative learning, agents may behave in a self-centred manner and not converge to an optimal global policy.
Appropriate reward design: it is important to design the reward function in such a way that agents take the desired behaviour, and also to take into account the structure of conflict and cooperation in a multi-agent environment.
2. non-stationarity and policy change: in multi-agent systems, the policies and behaviours of other agents may change. Such non-stationarity may also affect learning stability.
dynamic environment modelling: dynamic environment modelling is important for agents to understand and adapt to changes in their environment, and it is also important to design learning algorithms and policy update methods to be able to adapt to changes in the environment.
3. communication and shared information: in multi-agent systems, agents may need to share information and communicate with each other. If information is not shared adequately, it will be difficult for agents to co-operate properly with each other.
Shared messaging: it is important to design mechanisms for agents to share information and communicate with each other, and it is also important to consider shared messaging and information encoding methods to ensure effective information sharing.
4. learning stability and convergence: learning stability and convergence can be problematic in multi-agent systems when multiple agents are learning simultaneously. In particular, learning stability may be vulnerable in the presence of non-linearity and non-stationarity.
Introducing stabilisation methods: to improve learning stability, it is important to introduce stabilisation methods to improve the learning speed and convergence of the agent. For example, empirical playback or the introduction of randomness could be considered.
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“
コメント