HOPE Agents

Hierarchical Optimizing Policy Engine for AIngle AI agents.

Overview

HOPE Agents is a complete reinforcement learning framework for building autonomous AI agents that can:

• 🧠 Learn from experience using Q-Learning, SARSA, and TD algorithms
• 🎯 Plan hierarchically with goal decomposition and conflict resolution
• 🔮 Predict future states and detect anomalies
• 🤝 Coordinate with other agents through message passing and shared memory
• 💾 Persist state for checkpointing and transfer learning

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      HOPE Agent                             │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  Observation → State → Decision → Action → Learning         │
│                                                              │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐  │
│  │  Predictive  │  │ Hierarchical │  │    Learning      │  │
│  │    Model     │  │ Goal Solver  │  │     Engine       │  │
│  │              │  │              │  │                  │  │
│  │ • Anomaly    │  │ • Goals      │  │ • Q-Learning     │  │
│  │ • Forecast   │  │ • Planning   │  │ • SARSA          │  │
│  │ • Patterns   │  │ • Conflicts  │  │ • TD Learning    │  │
│  │              │  │              │  │ • Experience     │  │
│  └──────────────┘  └──────────────┘  └──────────────────┘  │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Features

✅ Complete (100%)

1. Learning Engine

• Algorithms: Q-Learning, SARSA, TD(λ), Expected SARSA
• Experience Replay: Prioritized replay buffer for efficient learning
• Exploration: Epsilon-greedy and Boltzmann exploration
• Value Functions: Tabular and linear function approximation

2. Hierarchical Goal Solver

• Goal Types: Achieve, Maintain, Avoid, Explore
• Decomposition: Automatic goal breakdown into subgoals
• Conflict Resolution: Detect and resolve goal conflicts
• Priorities: Support for goal prioritization

3. Predictive Model

• State Prediction: Forecast next states given actions
• Anomaly Detection: Statistical anomaly detection with z-scores
• Trajectory Planning: Multi-step lookahead
• Transition Model: Learn state transition dynamics

4. Multi-Agent Coordination

• Message Passing: Broadcast and direct messaging
• Shared Memory: Global key-value store for coordination
• Consensus: Voting-based group decision making
• Agent Registry: Dynamic agent registration/unregistration

5. State Persistence

• Formats: JSON, Binary, MessagePack
• Compression: Optional compression for efficient storage
• Checkpointing: Automatic periodic checkpointing
• Transfer Learning: Save and load trained agents

Quick Start

Simple Reactive Agent

use hope_agents::{Agent, SimpleAgent, Goal, Observation, Rule, Condition, Action};

// Create a simple reactive agent
let mut agent = SimpleAgent::new("sensor_monitor");

// Add a rule: if temperature > 30, alert
let rule = Rule::new(
    "high_temp",
    Condition::above("temperature", 30.0),
    Action::alert("Temperature too high!"),
);
agent.add_rule(rule);

// Process observations
let obs = Observation::sensor("temperature", 35.0);
agent.observe(obs.clone());
let action = agent.decide();
let result = agent.execute(action.clone());
agent.learn(&obs, &action, &result);

HOPE Agent with Learning

use hope_agents::{HopeAgent, Observation, Goal, Priority, Outcome, ActionResult};

// Create a HOPE agent with learning, prediction, and hierarchical goals
let mut agent = HopeAgent::with_default_config();

// Set a goal
let goal = Goal::maintain("temperature", 20.0..25.0)
    .with_priority(Priority::High);
agent.set_goal(goal);

// Agent loop with reinforcement learning
for episode in 0..100 {
    let obs = Observation::sensor("temperature", 22.0);
    let action = agent.step(obs.clone());

    // Execute action in environment and get reward
    let reward = 1.0;
    let next_obs = Observation::sensor("temperature", 21.0);
    let result = ActionResult::success(&action.id);

    let outcome = Outcome::new(action, result, reward, next_obs, false);
    agent.learn(outcome);
}

// Check statistics
let stats = agent.get_statistics();
println!("Episodes: {}", stats.episodes_completed);
println!("Success rate: {:.2}%", stats.success_rate * 100.0);

Multi-Agent Coordination

use hope_agents::{AgentCoordinator, HopeAgent, Message, Observation};
use std::collections::HashMap;

// Create coordinator
let mut coordinator = AgentCoordinator::new();

// Register agents
let agent1 = HopeAgent::with_default_config();
let agent2 = HopeAgent::with_default_config();

let id1 = coordinator.register_agent(agent1);
let id2 = coordinator.register_agent(agent2);

// Broadcast message
coordinator.broadcast(Message::new("update", "System status changed"));

// Step all agents
let mut observations = HashMap::new();
observations.insert(id1, Observation::sensor("temp", 20.0));
observations.insert(id2, Observation::sensor("humidity", 60.0));

let actions = coordinator.step_all(observations);

State Persistence

use hope_agents::{HopeAgent, AgentPersistence, CheckpointManager};
use std::path::Path;

let mut agent = HopeAgent::with_default_config();

// Train the agent...

// Save agent state
agent.save_to_file(Path::new("agent_state.json")).unwrap();

// Later, load agent state
let loaded_agent = HopeAgent::load_from_file(Path::new("agent_state.json")).unwrap();

// Or use checkpoint manager for automatic checkpointing
let mut manager = CheckpointManager::new(Path::new("checkpoints"), 5)
    .with_interval(1000);

// During training
for step in 0..10000 {
    // ... train agent ...

    if manager.should_checkpoint(step) {
        manager.save_checkpoint(&agent, step).unwrap();
    }
}

Operation Modes

HOPE agents support multiple operation modes:

• Exploration: High exploration rate for discovering new strategies
• Exploitation: Use learned knowledge for optimal performance
• GoalDriven: Balance exploration with goal achievement
• Adaptive: Automatically switch modes based on performance

agent.set_mode(OperationMode::Exploration);  // High exploration
agent.set_mode(OperationMode::Exploitation); // Pure exploitation
agent.set_mode(OperationMode::Adaptive);     // Auto-adjust

Goal Management

Goal Types

// Achieve a target value
let goal = Goal::achieve("temperature", 25.0);

// Maintain value in range
let goal = Goal::maintain("humidity", 40.0..60.0);

// Avoid certain values
let goal = Goal::avoid("pressure", 100.0);

// Explore and discover
let goal = Goal::explore("new_area");

Hierarchical Goals

Goals are automatically decomposed into subgoals:

let parent = Goal::achieve("optimize_system", 1.0)
    .with_priority(Priority::High);

let goal_id = agent.set_goal(parent);

// Automatically creates subgoals for different aspects
let active_goals = agent.active_goals();

Consensus and Coordination

Agents can make group decisions through voting:

let mut coordinator = AgentCoordinator::new();

// Create proposal
let proposal_id = coordinator.create_proposal(
    "new_policy",
    "Should we adopt the new temperature policy?"
);

// Agents vote
// ... (voting happens through message passing) ...

// Check consensus
match coordinator.get_consensus(&proposal_id) {
    Some(ConsensusResult::Decided { approved, votes_for, votes_against, .. }) => {
        println!("Decision: {}", if approved { "Approved" } else { "Rejected" });
        println!("Votes: {} for, {} against", votes_for, votes_against);
    }
    _ => println!("Voting in progress..."),
}

Configuration

IoT Mode

Optimized for resource-constrained devices:

use hope_agents::AgentConfig;

let config = AgentConfig::iot_mode();
let agent = SimpleAgent::with_config("iot_agent", config);

// Features:
// - Limited memory (128KB)
// - Disabled learning
// - Reduced buffer sizes

Custom Configuration

use hope_agents::{HopeConfig, LearningConfig, PredictiveConfig, LearningAlgorithm};

let config = HopeConfig {
    learning: LearningConfig {
        learning_rate: 0.1,
        discount_factor: 0.95,
        algorithm: LearningAlgorithm::QLearning,
        epsilon: 0.2,
        ..Default::default()
    },
    predictive: PredictiveConfig {
        history_size: 500,
        ..Default::default()
    },
    anomaly_sensitivity: 0.8,
    auto_decompose_goals: true,
    ..Default::default()
};

let agent = HopeAgent::new(config);

Performance

HOPE Agents is designed for high performance:

• 1000+ steps/second on modern hardware
• Efficient memory usage with configurable limits
• Incremental learning with no batch processing required
• Lock-free coordination for multi-agent scenarios

Benchmarks

Run benchmarks with:

cargo bench

Testing

Comprehensive test suite with 133 tests covering:

• Unit tests for all components
• Integration tests for complete workflows
• Multi-agent coordination scenarios
• Persistence roundtrip tests
• Performance tests

Run tests:

# All tests
cargo test

# Specific module
cargo test coordination
cargo test persistence

# Integration tests
cargo test --test integration_test

# With output
cargo test -- --nocapture

Examples

See the tests/integration_test.rs file for comprehensive examples demonstrating:

• Simple agent workflows
• Learning cycles with multiple episodes
• Multi-agent coordination
• Consensus mechanisms
• State persistence and checkpointing
• Anomaly detection
• Operation mode switching

API Documentation

Full API documentation is available at docs.rs/hope_agents.

Generate local documentation:

cargo doc --open

Integration with AIngle

HOPE Agents integrates seamlessly with the AIngle network:

// Observe network events
let obs = Observation::network_event("node_joined", node_id);

// Execute actions on the network
let action = Action::send_message("Hello, network!");

// Store data in AIngle
let action = Action::store_data(serde_json::to_string(&data).unwrap());

Roadmap

Future enhancements (beyond 100%):

• Deep Q-Networks (DQN) support
• Policy gradient methods (PPO, A3C)
• Multi-objective optimization
• Distributed training
• Web assembly support
• Python bindings

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Citation

If you use HOPE Agents in your research, please cite:

@software{hope_agents,
  title = {HOPE Agents: Hierarchical Optimizing Policy Engine},
  author = {Apilium Technologies},
  year = {2025},
  url = {https://github.com/ApiliumCode/aingle}
}

Support

• Documentation: https://docs.rs/hope_agents
• Issues: https://github.com/ApiliumCode/aingle/issues

Status: ✅ 100% Complete

All core features implemented, tested, and documented.