from neurenix.agent import Agent, Environment, MultiAgent

# Create a custom agent
class MyAgent(Agent):
    def act(self, observation):
        # Implement action selection logic
        return action
    
    def learn(self, experience):
        # Implement learning logic
        pass

# Create a custom environment
class MyEnvironment(Environment):
    def step(self, actions):
        # Process actions and return results
        return {
            "rewards": rewards,
            "done": done,
            "info": info
        }
    
    def observe(self, agent):
        # Return observation for agent
        return observation

# Create and use agents
agent = MyAgent(name="agent-1")
env = MyEnvironment()

# Get observation and act
observation = env.observe(agent)
action = agent.act(observation)

Agent → act(observation) → Action
  ↓
learn(experience)
  ↓
Updated Agent

Environment → step(actions) → Results (rewards, done, info)
            → observe(agent) → Observation

MultiAgent → coordinates → Multiple Agents + Environment

class RLAgent(Agent):
    def __init__(self, name=None):
        super().__init__(name)
        self.policy = None  # Your policy network
        self.value = None   # Your value network
    
    def act(self, observation):
        # Use policy to select action
        return self.policy.forward(observation)
    
    def learn(self, experience):
        # Update policy based on experience
        state, action, reward, next_state = experience
        # Your learning algorithm (PPO, DQN, etc.)

agents = [MyAgent(f"agent-{i}") for i in range(5)]
env = MyEnvironment()
mas = MultiAgent(agents, env)

# Run simulation
observations = mas.reset()
for step in range(1000):
    results = mas.step()
    if results["done"]:
        break

class AutonomousAgent(Agent):
    def __init__(self, goal, name=None):
        super().__init__(name)
        self.goal = goal
    
    def act(self, observation):
        # Plan actions based on goal
        return self.plan_action(observation, self.goal)

from neurenix.agent import Agent, Environment, MultiAgent