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IQL Network Support - Implementation Summary

✅ Completed Implementation

1. Modular Q-Learning Backends

  • TabularQLearning: Uses defaultdict Q-tables with smooth policy updates
  • NetworkQLearning: PyTorch neural networks with target networks
  • Unified Interface: Abstract QLearningBackend base class

2. Algorithm Updates

  • Added network parameter to TwoPhaseTimescaleIQL constructor
  • Added state_dim parameter for neural network input dimension
  • Maintained backward compatibility with existing tabular mode
  • Smooth policy updates using softmax instead of epsilon-greedy

3. Command-Line Support

  • Added --network flag to enable neural network mode
  • Added --state-dim parameter for state vector dimension
  • Updated help documentation

4. Documentation Updates

  • Updated README.md with network mode examples
  • Added installation requirements for PyTorch
  • Documented when to use each mode
  • Provided usage examples for both modes

🚀 Usage Examples

Tabular Mode (Default)

# Small discrete environments
python run_iql.py --mode train --phase1-episodes 500 --phase2-episodes 500 --map simple_map

# Visualization
python run_iql.py --mode visualize --load q_values.pkl --map simple_map

Neural Network Mode

# Large or complex environments
python run_iql.py --mode train --phase1-episodes 500 --phase2-episodes 500 --map complex_map --network --state-dim 6

# Training with rendering
python run_iql.py --mode train --map simple_map --network --state-dim 4 --render

# Visualization (network mode)
python run_iql.py --mode visualize --load q_values_nn.pkl --map complex_map --network --state-dim 6

🔧 Key Features

Tabular Mode

  • ✅ Fast for small state spaces (< 10,000 states)
  • ✅ Exact Q-value storage and retrieval
  • ✅ Complete policy analysis and debugging
  • ✅ Deterministic behavior
  • ❌ Limited to discrete state spaces

Neural Network Mode

  • ✅ Scalable to large state spaces (> 10,000 states)
  • ✅ Generalization across similar states
  • ✅ Handles continuous or high-dimensional states
  • ✅ Memory efficient for large problems
  • ❌ Approximate Q-values
  • ❌ Requires more training data

📊 Files Modified

New Files:

  • q_learning_backends.py - Modular backend system
  • run_iql.py - Updated runner script with network support
  • Test files for validation

Modified Files:

  • iql_timescale_algorithm.py - Added modular backend support
  • main.py - Added network command-line arguments
  • README.md - Updated documentation
  • Various import fixes for proper module structure

🧪 Testing

The implementation includes several test scripts:

  1. test_network_short.py - Comprehensive unit tests
  2. demo_network.py - Interactive demonstration
  3. test_integration_short.py - Full pipeline testing
  4. validate_network.py - Implementation validation

💡 Choosing the Right Mode

Use Tabular Mode when:

  • State space is small and discrete
  • You need exact Q-values
  • You want complete policy analysis
  • Fast training is important

Use Neural Network Mode when:

  • State space is large (> 10,000 states)
  • States have continuous components
  • You need generalization across states
  • Memory efficiency is important

🎯 Sequential Training Feature

The implementation also includes the improved Phase 1 training that:

  • Learns individual conservative models for each human
  • Uses targeted robot actions against specific humans
  • Provides more accurate human behavior models
  • Follows the mathematical formulation correctly

✨ Ready for Production

The IQL Timescale Algorithm now supports both tabular and neural network modes, making it suitable for a wide range of applications from simple gridworlds to complex multi-agent environments.