Documentation Index
Fetch the complete documentation index at: https://mintlify.com/MilesONerd/neurenix/llms.txt
Use this file to discover all available pages before exploring further.
Overview
The MultiAgent class provides functionality for coordinating multiple agents, enabling them to interact with each other and with a shared environment.
Class Definition
class MultiAgent:
def __init__(self, agents: List[Agent], environment: Environment)
Parameters
List of agents in the system.
Shared environment for the agents.
Properties
step_count
@property
def step_count(self) -> int
Core Methods
step
def step(self) -> Dict[str, Any]
- Gets observations for each agent from the environment
- Has each agent select an action based on its observation
- Applies all actions to the environment
- Returns the results
Dictionary containing observations, actions, rewards, and done flags for each agent:
observations: Dict mapping agent IDs to observationsactions: Dict mapping agent IDs to actionsrewards: Dict mapping agent IDs to rewardsdone: Boolean indicating if the episode is completeinfo: Additional information
reset
def reset(self) -> Dict[str, Any]
- Resets the environment
- Resets each agent
- Returns the initial observations
Dictionary containing initial observations for each agent.
Agent Management
add_agent
def add_agent(self, agent: Agent) -> None
remove_agent
def remove_agent(self, agent_id: str) -> Optional[Agent]
ID of the agent to remove.
The removed agent, or None if no agent with the given ID was found.
len
Get the number of agents in the system.
Example Usage
import neurenix as nx
from neurenix.agent import Agent, MultiAgent, Environment
# Define a custom environment
class GridWorld(Environment):
def __init__(self, width, height):
super().__init__()
self.width = width
self.height = height
self.agent_positions = {}
def reset(self):
# Reset environment state
self.agent_positions = {}
return {}
def observe(self, agent):
# Return observation for a specific agent
position = self.agent_positions.get(agent.id, (0, 0))
return nx.Tensor([position[0], position[1]])
def step(self, actions):
# Apply all agent actions
rewards = {}
for agent_id, action in actions.items():
# Update agent position based on action
# Calculate rewards
rewards[agent_id] = self.calculate_reward(agent_id, action)
return {
"rewards": rewards,
"done": self.is_terminal(),
"info": {}
}
# Create agents
agent1 = MyAgent(name="Agent-1")
agent2 = MyAgent(name="Agent-2")
agent3 = MyAgent(name="Agent-3")
# Create environment
env = GridWorld(width=10, height=10)
# Create multi-agent system
mas = MultiAgent(
agents=[agent1, agent2, agent3],
environment=env
)
print(f"Number of agents: {len(mas)}")
# Run simulation
obs = mas.reset()
for episode in range(100):
obs = mas.reset()
done = False
episode_rewards = {agent.id: 0 for agent in mas.agents}
while not done:
# Step the multi-agent system
results = mas.step()
# Track rewards
for agent_id, reward in results["rewards"].items():
episode_rewards[agent_id] += reward
# Check if done
done = results["done"]
# Train agents
for agent in mas.agents:
agent.learn(results)
print(f"Episode {episode}: {episode_rewards}")
# Add a new agent dynamically
new_agent = MyAgent(name="Agent-4")
mas.add_agent(new_agent)
print(f"Agents after addition: {len(mas)}")
# Remove an agent
removed = mas.remove_agent("Agent-1")
print(f"Removed: {removed.name}")
print(f"Remaining agents: {len(mas)}")
Use Cases
Cooperative Tasks
Multiple agents working together to achieve a common goal
Competitive Games
Agents competing against each other in zero-sum games
Mixed Settings
Combination of cooperation and competition
Swarm Intelligence
Large numbers of simple agents exhibiting collective behavior