Implementation of ReAct by multi-agent systems

Implementation of ReAct by multi-agent systems

ReAct (Reasoning and Acting) described in “Overview of ReAct (Reasoning and Acting) and examples of its implementation” is one of the architectures of AI systems, which provides a framework for agents to solve problems and make decisions through a cycle of reasoning and action. In the implementation of ReAct in multi-agent systems, multiple agents work cooperatively or competitively, influencing each other to proceed with a task.

ReAct consists of two main components

• Reasoning: agents analyse the situation and reason to determine the best course of action.
• Acting: based on the results of reasoning, the agent actually acts.

This process is repeated, with the agent adapting to the environment, receiving the results as feedback and deciding on its next action.

In a multi-agent system (MAS) described in “An Introduction to Multiagent Systems“, multiple agents operate simultaneously to solve problems through cooperation or conflict.The following implementation steps can be considered when applying the ReAct architecture to a MAS

• Agent design: each agent has independent reasoning and action cycles. Agents can include the following components
  • Sensors: receive information from the environment.
  • Reasoning engine: analyses the information received and decides what action to take next.
  • Actuators: modules that perform actions.
• Interaction between agents: in multi-agent systems, information is often exchanged between agents, so that each agent needs to make inferences based on inputs from other agents. Possible methods of interaction include
  • Cooperative agents: where several agents work together to achieve one major goal (e.g. a team of robots working together to explore).
  • Competitive agents: where each agent competes and tries to achieve an individual goal (e.g. players in a game).
  • Neutral interactions: neutral relationships where there is neither competition nor cooperation (e.g. agents achieve goals separately).
• Execution of ReAct cycles: based on a structure where agents repeat reasoning and action individually, the following cycles can be implemented.
  1. Situation awareness (Reasoning): data is acquired from the environment and the agent analyses the situation. For example, it considers the behaviour of other agents and changes in the environment.
  2. Decision-making (Acting): based on the results of the analysis, the most appropriate action is selected. In some cases, it may choose a co-operative or competitive behaviour with other agents.
  3. Obtaining feedback: after executing an action, feedback is received from the environment and other agents to inform the next inference.
• Communication and co-ordination between agents: information exchange and co-ordination between agents are important elements in MAS. Communication takes place, for example, by
  • Messaging systems: agents send and receive messages to take coordinated action.
  • Shared states: several agents access the same environmental information and cooperate to achieve a goal.

Technologies and frameworks that can be used to implement ReAct in multi-agent systems include.

• JADE (Java Agent Development Framework): a framework supporting the development of multi-agent systems in Java.
• OpenAI Gym: an environment that can be used to train reinforcement learning agents. Also suitable for multi-agent reinforcement learning experiments.
• ROS (Robot Operating System): a framework for managing robot co-ordination and interaction.

Implementing ReAct in a multi-agent system allows agents to take appropriate action in response to their environment in a co-operative or competitive manner. For implementation, it is important to design an iterative cycle of situation awareness, decision-making, action execution and feedback, and communication and coordination between agents is also critical to the success of the system.

implementation example

A concrete implementation of the ReAct architecture in a multi-agent system (MAS) is shown below. Here, a scenario is created in which simple agents co-operate to achieve a goal using Python and Mesa, a multi-agent system framework.

1. installation of the required packages: first, install the multi-agent system framework called Mesa.

pip install mesa

2. basic ReAct agent definition: based on the ReAct architecture, create a class in which agents repeat the cycle of reasoning (Reasoning) and acting (Acting). Agents have a mechanism for sharing information in order to cooperate and achieve their goals.

from mesa import Agent
import random

# agent class
class MyAgent(Agent):
    """Agents with ReAct."""
    
    def __init__(self, unique_id, model):
        super().__init__(unique_id, model)
        self.position = (random.randint(0, 10), random.randint(0, 10))  # Random initial position

    def step(self):
        """Agent reasoning and behaviour."""
        # Reasoning: calculate distance to target position
        target_position = self.model.target_position
        distance = ((self.position[0] - target_position[0]) ** 2 + 
                    (self.position[1] - target_position[1]) ** 2) ** 0.5
        
        # Acting: moving closer to the target
        if distance > 1:
            self.move_towards_target(target_position)
        else:
            self.reach_target()

    def move_towards_target(self, target_position):
        """Moving towards the target"""
        x_move = 1 if self.position[0] < target_position[0] else -1
        y_move = 1 if self.position[1] < target_position[1] else -1
        self.position = (self.position[0] + x_move, self.position[1] + y_move)

    def reach_target(self):
        """Action when the target is reached."""
        print(f"Agent {self.unique_id} has reached its target.")

3. defining the model: the model in which the agents interact within the simulation environment is then defined. This model manages the initialisation of the agents and the progression of the simulation.

from mesa import Model
from mesa.time import RandomActivation
from mesa.space import MultiGrid

class MyModel(Model):
    """Models of multi-agent systems."""
    
    def __init__(self, num_agents, target_position):
        self.num_agents = num_agents
        self.target_position = target_position
        self.schedule = RandomActivation(self)
        self.grid = MultiGrid(10, 10, True)  # 10x10 grid.
        
        # Agent initialisation
        for i in range(self.num_agents):
            a = MyAgent(i, self)
            self.schedule.add(a)
            x = self.random.randrange(self.grid.width)
            y = self.random.randrange(self.grid.height)
            self.grid.place_agent(a, (x, y))
    
    def step(self):
        """Advance one step."""
        self.schedule.step()

4. execution and simulation: the model is then executed to see how the agent moves towards the target.

# Setting up the model
num_agents = 5  # Number of agents.
target_position = (5, 5)  # Target location

# Instantiating the model
model = MyModel(num_agents, target_position)

# Running the simulation
for i in range(10):  # Take ten steps forward.
    print(f"Step {i+1}")
    model.step()

5. execution result: when this code is executed, the agent moves towards the goal at each step and the console displays that the goal has been reached; the agent is able to achieve the goal because it repeats reasoning and action within the step() method.

Step 1 
Step 2 
... 
Agent 0 has reached the target.

6. extension ideas: this implementation can be further extended. Examples include.

• Cooperative behaviour: when several agents work together to achieve a single goal, an algorithm can be added that allows the agents to share information with each other and achieve the goal more efficiently.
• Competitive behaviour: scenarios can be changed where each agent competes for a goal independently.
• Dynamic environment: if the environment changes dynamically (e.g. the appearance of obstacles), agents can adjust their reasoning and behaviour to adapt to these changes.

Application examples

The implementation of ReAct by multi-agent systems (MAS) is a useful approach in many different areas. Specific application examples are described below.

1. cooperative tasks of autonomous robots: the ReAct architecture plays an important role in scenarios where autonomous robots cooperate to accomplish tasks. Specifically, multiple robots can be considered to cooperate to achieve a goal by reasoning (Reasoning) and co-ordinating their actions (Acting) with each other.

• Example (transporting goods in a warehouse): several robots perform the task of transporting goods in a warehouse. Each robot needs to move the goods to a target point and, using the ReAct architecture, the robots act as follows
  • Reasoning: each robot calculates the distance between its current position and the destination and finds the shortest path. They also share information in real-time to avoid collisions with other robots.
  • Acting: based on the results of inference, the robots move. The robots cooperate with each other to avoid duplication of work and to accomplish tasks efficiently.

2. traffic simulation: agents using the ReAct architecture are also important in traffic simulation. Specifically, this could involve vehicles (agents) interacting on an urban road network and moving towards a target point.

• Example (congestion management): several vehicles drive towards a target point in a city. The vehicles cooperate with each other to avoid congestion and use ReAct to select the best route.
  • Reasoning: each vehicle collects information on other vehicles’ positions, traffic signals and congestion, decides on its next course of action and dynamically changes its route according to traffic conditions in real time.
  • Acting: vehicles co-ordinate with other vehicles to reach their destinations safely and efficiently. Communication and co-ordination are key elements, as vehicles co-operate with each other to avoid traffic jams.

3. co-operation between robots in manufacturing: in manufacturing automation, multiple robots co-operate on a production line using the ReAct architecture, where each robot is responsible for a specific task and cooperates to complete the product.

• Example (automated assembly line): robots assemble parts on a production line; based on ReAct, robots cooperate with each other and share tasks in real-time.
  • Reasoning: each robot understands the work status of the other robots, decides which parts to hold and when, and distributes the tasks evenly to avoid overload.
  • Acting: robots move and assemble parts according to the decided actions. The progress of the work is monitored and adjustments are made if necessary.

4. agent-based simulation in healthcare: multi-agent systems using the ReAct architecture are also useful in healthcare. Specifically, multiple agents (doctors, nurses, patients, etc.) could work together to determine the most appropriate medical action.

• Example (patient care in a hospital): in a simulation of managing patient care in a hospital, doctors and nurses work together to provide appropriate treatment to patients.
  • Reasoning: the doctor decides on a treatment plan based on the patient’s symptoms, while the nurse monitors the patient’s condition, takes the necessary actions and adjusts behaviour according to the patient’s condition.
  • Acting: the nurse carries out treatment according to the doctor’s instructions and reports on the patient’s condition. The doctor uses this information to further adjust the treatment.

5. multi-agent strategy in games: the ReAct architecture can also be applied to strategic cooperation between agents in multi-player games, allowing players (agents) to compete and cooperate to win.

• Example (strategic simulation game): in a strategic simulation game, multiple agents cooperate to manage resources and formulate strategies to defeat the enemy. Agents choose their own actions in real time and work with other agents to gain an advantage in the battlefield.
  • Reasoning: each agent formulates strategies based on the progression of the game, allocating resources and deciding when to attack.
  • Acting: based on the strategy, agents perform actions such as attack, defence and resource management.

The ReAct architecture can be applied to the design of multi-agent systems, which is useful in many different areas. In particular, in scenarios where cooperation is important, agents can reason independently and take action based on the results of their reasoning, enabling them to achieve their goals efficiently. As shown in the examples above, ReAct has been implemented in various fields, including robotics, traffic management, manufacturing, medicine and gaming.

reference book

Reference books related to the ReAct architecture and multi-agent systems are described.

1. ‘Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence’ by Gerhard Weiss
– Abstract: A comprehensive book on the fundamental theory and implementation of multi-agent systems. It provides applied knowledge on the design of collaborative agent systems, communication between agents, decision-making processes and the ReAct architecture.

2. ‘An Introduction to MultiAgent Systems’ by Michael Wooldridge
– Abstract: An introduction to multi-agent systems, ideal for learning basic concepts and algorithms; also covers theories of agent reasoning and behaviour relevant to the ReAct architecture.

3. ‘Autonomous Agents and Multi-Agent Systems: A Methodological Introduction’ by Rafael H. Bordini, Mehdi Dastani, Joris N. H. L. A. Kakkar
– Abstract: Provides a methodological approach to the study of autonomous agents and their co-operative behaviour. Specific guidelines for design, reasoning and behaviour in multi-agent systems are provided.

4. ‘Intelligent Agents: Theory and Applications’ by P. K. Sinha and S. R. Das
– Abstract: Provides in-depth insights into the theory and applications of intelligent agents; a useful book for building the theoretical foundations needed to implement ReAct.

5. ‘Reinforcement Learning: An Introduction’ by Richard S. Sutton and Andrew G. Barto
– Abstract: This book provides the basic theory of Reinforcement Learning (Reinforcement Learning) in order to apply the ReAct architecture. It helps to understand the process by which agents learn behaviour while interacting with their environment.

6. ‘Game Theory for Applied Economists’ by Robert Gibbons
– Abstract: An introduction to game theory for understanding strategic interactions in multi-agent systems. In particular, it provides a theory of decision-making among agents with co-operative behaviour, such as ReAct.

7. ‘Handbook of Multi-Agent Systems: Semantics and Dynamics of Organisational Models’ by Gerhard Weiss
– Abstract: Focusing on the dynamic behaviour of organisational agents and the organisation theory of multi-agent systems, the book provides useful organisational knowledge for designing systems such as ReAct.

8. ‘Distributed Artificial Intelligence’ by P. M. D. W. P. R. D. G. H. S. T. M. M. S. N.
– Abstract: Theoretical foundations for distributed artificial intelligence can be learned. It helps to understand the key theories underpinning the processes of co-operation, conflict resolution and decision-making between agents.

9. ‘Artificial Intelligence: A Modern Approach’ by Stuart Russell and Peter Norvig
– Abstract: The book covers a wide range of theories and techniques related to artificial intelligence. In particular, the section on agent systems will be useful for a better understanding of the ReAct architecture.