Semismooth Newton Methods for Risk-Averse Dynamic Programming

This repository contains Python code for reproducing the benchmark experiments presented in the paper: "Semismooth Newton Methods for Risk-Averse Dynamic Programming" arXiv:2501.13612

🧠 riskaverse_DP

Risk-Averse Dynamic Programming via Semismooth Newton Methods This repository provides a Python implementation of novel semismooth Newton algorithms to solve risk-averse Markov Decision Processes (MDPs) under Markovian risk measures—particularly the Conditional Value at Risk (CVaR) and Mean-Upper-Semideviation of order 1 (MUS1).

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⭐ Features

• Implements three semismooth Newton methods for risk-averse MDPs:

  1. SNM I: Sequential risk-neutral MDP solves with perturbed transitions.
  2. SNM II: A risk-averse policy-iteration–style Newton solve.
  3. SNM III: A linearized, piecewise-smooth Newton update.

• Includes risk-averse optimistic policy iteration as a comparative baseline.

• Supports CVaR and MUS1 as risk measures, with theoretical guarantees of quadratic convergence under certain conditions.

• Benchmarked on synthetic MDPs with varied sizes and discount factors, with empirical performance data included.

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🚀 Installation

Follow these steps to set up your development environment and install the package:

1. Create and activate a Conda environment (recommended):

   conda create -n riskaverse python=3.10
   conda activate riskaverse

2. Install project dependencies:

   pip install -r requirements.txt

3. Install the package in editable mode:

   pip install -e . --use-pep517

4. Verify installation:

   python -c "from riskaverse_DP.mdps import mdp; print('Success')"

This will ensure your riskaverse_DP package is installed and ready for development.

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📦 Usage

Basic usage pattern:

import numpy as np
from riskaverse_DP.mdps import mdp

n_states = 10
n_actions = 3

# Initialize with transition and stage-cost data
my_mdp = mdp(n_states, n_actions)

gamma_ = 0.9
alpha_ = 0.1
v_0 = np.zeros(n_states, )

from riskaverse_DP.CVaR_solvers import SNMI

my_SNMI = SNMI(gamma_, alpha_)

# Solve using one of the methods: "SNMI", "SNMII", "SNMIII", or "OPI"
v_opt, res = my_SNMI.solve(my_mdp, v_0)

Check the examples/ folder for detailed scripts on synthetic MDPs.

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📈 Benchmark Results

Experiments on MDPs with sizes up to 100 states and diversified action spaces show:

• SNM methods reach convergence in fewer than 10 iterations, whereas risk-averse VI needs over 150.
• CPU times: SNM I and SNM III are most efficient; SNM II may face numeric issues depending on solver accuracy.

Mean-Upper-Semideviation (Order 1)

This project implements the Mean-Upper-Semideviation risk measure of order 1 (MUS1), a coherent risk measure used in risk-averse optimization and decision-making.

Given a random variable X and a parameter kappa in [0, 1], the MUS1 risk measure is defined as:

rho(X) = E[X] + kappa * E[(X - E[X])_+]

Here, (x)_+ denotes the positive part of x, i.e., max(x, 0). The parameter kappa controls the degree of risk sensitivity to values above the mean.

For a finite probability space with outcomes X_i and probabilities p_i, the MUS1 risk measure has the following dual representation as a linear program (LP):

Linear Program:

Maximize:     sum_i p_i * xi_i * X_i

Subject to:   sum_i p_i * xi_i = 1
              0 <= xi_i <= 1 + kappa   for all i

This LP computes the worst-case expected value under a distorted probability distribution defined by the weights xi_i * p_i, consistent with the MUS1 risk measure.

MUS1 SNMs	MUS1 OPI

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📚 Background & Theory

For a full theoretical foundation—including proofs of convergence and algorithmic derivations—see the arXiv preprint “Semismooth Newton Methods for Risk-Averse Markov Decision Processes”.

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🤝 Contributing

Contributions are welcome! Feel free to open issues or pull requests for:

• New risk measures or benchmarks
• Extensions to large/continuous state spaces
• Performance optimizations or parallel implementations

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📄 License

This project is released under the MIT License. See LICENSE for complete details.