Breaking the curse of dimensionality in POMDPs and games with sampling-based online planning

Assistant Professor Zachary Sunberg

University of Colorado Boulder

April 1, 2026

Autonomous Decision and Control Laboratory

cu-adcl.org

Algorithmic Contributions
- Scalable algorithms for partially observable Markov decision processes (POMDPs)
- Motion planning with safety guarantees
- Game theoretic algorithms
Theoretical Contributions
- Particle POMDP approximation bounds
Applications
- Space Domain Awareness
- Autonomous Driving
- Autonomous Aerial Scientific Missions
- Search and Rescue
- Space Exploration
- Ecology
Open Source Software
- POMDPs.jl Julia ecosystem

PI: Prof. Zachary Sunberg

PhD Students

Tweet by Nitin Gupta

29 April 2018

https://twitter.com/nitguptaa/status/990683818825736192

Example 1: Autonomous Driving

2018

Example 1:
Autonomous Driving

2025

Example 2: Meteorology

Video: Eric Frew

Example 2: Meteorology

Example 3: Search and Rescue

What do they have in common?

Driving: what are the other road users going to do?

Tornado Forecasting: what is going on in the storm?

Search and Rescue: where is the lost person?

All are sequential decision-making problems with uncertainty!

All can be modeled as a POMDP (with a very large state and observation spaces).

Outline

The Promise and Curse of POMDPs
Breaking the Curse
Applications
Multiple Agents

Part I: The Promise and Curse of POMDPs

Types of Uncertainty

Aleatory

Epistemic (Static)

Epistemic (Dynamic)

Strategic

MDP

POMDP

Game

Markov Decision Process (MDP)

\(\mathcal{S}\) - State space
\(T:\mathcal{S}\times \mathcal{A} \times\mathcal{S} \to \mathbb{R}\) - Transition probability distribution
\(\mathcal{A}\) - Action space
\(R:\mathcal{S}\times \mathcal{A} \to \mathbb{R}\) - Reward

Aleatory

\([x, y, z,\;\; \phi, \theta, \psi,\;\; u, v, w,\;\; p,q,r]\)

\(\mathcal{S} = \mathbb{R}^{12}\)

\(\mathcal{S} = \mathbb{R}^{12} \times \mathbb{R}^\infty\)

\[\underset{\pi:\, \mathcal{S} \to \mathcal{A}}{\text{maximize}} \quad \text{E}\left[ \sum_{t=0}^\infty R(s_t, a_t) \right]\]

Reinforcement Learning

\(\mathcal{S}\) - State space
\(T:\mathcal{S}\times \mathcal{A} \times\mathcal{S} \to \mathbb{R}\) - Transition probability distribution
\(\mathcal{A}\) - Action space
\(R:\mathcal{S}\times \mathcal{A} \to \mathbb{R}\) - Reward

Aleatory

Epistemic (Static)

\([x, y, z,\;\; \phi, \theta, \psi,\;\; u, v, w,\;\; p,q,r]\)

\(\mathcal{S} = \mathbb{R}^{12}\)

\(\mathcal{S} = \mathbb{R}^{12} \times \mathbb{R}^\infty\)

Partially Observable Markov Decision Process (POMDP)

\(\mathcal{S}\) - State space
\(T:\mathcal{S}\times \mathcal{A} \times\mathcal{S} \to \mathbb{R}\) - Transition probability distribution
\(\mathcal{A}\) - Action space
\(R:\mathcal{S}\times \mathcal{A} \to \mathbb{R}\) - Reward
\(\mathcal{O}\) - Observation space
\(Z:\mathcal{S} \times \mathcal{A}\times \mathcal{S} \times \mathcal{O} \to \mathbb{R}\) - Observation probability distribution

Aleatory

Epistemic (Static)

Epistemic (Dynamic)

\([x, y, z,\;\; \phi, \theta, \psi,\;\; u, v, w,\;\; p,q,r]\)

\(\mathcal{S} = \mathbb{R}^{12}\)

\(\mathcal{S} = \mathbb{R}^{12} \times \mathbb{R}^\infty\)

\begin{aligned} & \mathcal{S} = \mathbb{Z} \quad \quad \quad ~~ \mathcal{O} = \mathbb{R} \\ & s' = s+a \quad \quad o \sim \mathcal{N}(s, |s-10|) \\ & \mathcal{A} = \{-10, -1, 0, 1, 10\} \\ & R(s, a) = \begin{cases} 100 & \text{ if } a = 0, s = 0 \\ -100 & \text{ if } a = 0, s \neq 0 \\ -1 & \text{ otherwise} \end{cases} & \\ \end{aligned}

State

Timestep

Accurate Observations

Goal: \(a=0\) at \(s=0\)

Optimal Policy

Localize

\(a=0\)

POMDP Example: Light-Dark

Solving a POMDP

Environment

Belief Updater

Planner

\(a = +10\)

True State

\(s = 7\)

Observation \(o = -0.21\)

\(b\)

\[b_t(s) = P\left(s_t = s \mid b_0, a_0, o_1 \ldots a_{t-1}, o_{t}\right)\]

\[ = P\left(s_t = s \mid b_{t-1}, a_{t-1}, o_{t}\right)\]

\(Q(b, a)\)

\(O(|\mathcal{S}|^2)\)

Why are POMDPs difficult?

Curse of History
Curse of dimensionality
1. State space
2. Observation space
3. Action space

Tree size: \(O\left(\left(|A||O|\right)^D\right)\)

Curse of Dimensionality

\(d\) dimensions, \(k\) segments \(\,\rightarrow \, |S| = k^d\)

1 dimension

e.g. \(s = x \in S = \{1,2,3,4,5\}\)

\(|S| = 5\)

2 dimensions

e.g. \(s = (x,y) \in S = \{1,2,3,4,5\}^2\)

\(|S| = 25\)

3 dimensions

e.g. \(s = (x,y,x_h) \in S = \{1,2,3,4,5\}^3\)

\(|S| = 125\)

(Discretize each dimension into 5 segments)

\(x\)

\(y\)

\(x_h\)

Part II: Breaking the Curse

Integration

Find \(\underset{s\sim b}{E}[f(s)]\)

\[=\sum_{s \in S} f(s) b(s)\]

Monte Carlo Integration

\(Q_N \equiv \frac{1}{N} \sum_{i=1}^N f(s_i)\)

\(s_i \sim b\) i.i.d.

\(\text{Var}(Q_N) = \text{Var}\left(\frac{1}{N} \sum_{i=1}^N f(s_i)\right)\)

\(= \frac{1}{N^2} \sum_{i=1}^N\text{Var}\left(f(s_i)\right)\)

\(= \frac{1}{N} \text{Var}\left(f(s_i)\right)\)

(Bienayme)

Curse of dimensionality!

Inexact - but accuracy has no curse of dimensionality!

Particle Filter POMDP Approximation

\[b(s) \approx \sum_{i=1}^N \delta_{s}(s_i)\; w_i\]

[Sunberg and Kochenderfer, ICAPS 2018, T-ITS 2022]

How do we prove convergence?

Key 1: Self Normalized Infinite Renyi Divergence Concentation

\(\mathcal{P}\): state distribution conditioned on observations (belief)

\(\mathcal{Q}\): marginal state distribution (proposal)

Key 2: Sparse Sampling

Expand for all actions (\(\left|\mathcal{A}\right| = 2\) in this case)

...

Expand for all \(\left|\mathcal{S}\right|\) states

\(C=3\) states

PF Approximation Accuracy

\[|Q_{\mathbf{P}}^*(b,a) - Q_{\mathbf{M}_{\mathbf{P}}}^*(\bar{b},a)| \leq \epsilon \quad \text{w.p. } 1-\delta\]

For any \(\epsilon>0\) and \(\delta>0\), if \(C\) (number of particles) is high enough,

[Lim, Becker, Kochenderfer, Tomlin, & Sunberg, JAIR 2023]

No direct dependence on \(|\mathcal{S}|\) or \(|\mathcal{O}|\)!

Two Caveats

Number of particles is too big for practical safety guarantees.
Depends on the Renyi Divergence \(d_\infty(\mathcal{P} \Vert \mathcal{Q})\), which often grows in high-dimensional problems.

Possible Solution to 2:

Rao-Blackwellized POMDP Planning

\(p(s_t \mid h_t)\)

\(= p(s^a_t \mid s^p_t, h_t) \, p(s^p_t \mid h_t)\)

Analytical update (e.g. Kalman Filter)

Particle Filter

[Lee, Wray, Sunberg, Ahmed, ICRA 2026]

Why are POMDPs difficult?

Curse of History
Curse of dimensionality
1. State space
2. Observation space
3. Action space

Tree size: \(O\left(\left(|A|C\right)^D\right)\)

Solve simplified surrogate problem for policy deep in the tree

[Lim, Tomlin, and Sunberg, 2021]

Part III: Applications

Example 1: Autonomous Driving

POMDP Formulation

\(s=\left(x, y, \dot{x}, \left\{(x_c,y_c,\dot{x}_c,l_c,\theta_c)\right\}_{c=1}^{n}\right)\)

\(o=\left\{(x_c,y_c,\dot{x}_c,l_c)\right\}_{c=1}^{n}\)

\(a = (\ddot{x}, \dot{y})\), \(\ddot{x} \in \{0, \pm 1 \text{ m/s}^2\}\), \(\dot{y} \in \{0, \pm 0.67 \text{ m/s}\}\)

R(s, a, s') = \text{in\_goal}(s') - \lambda \left(\text{any\_hard\_brakes}(s, s') + \text{any\_too\_slow}(s')\right)

Ego external state

External states of other cars

Internal states of other cars (IDM Parameters)

External states of other cars

Actions shielded (based only on external states) so they can never cause crashes
Braking action always available

Efficiency

Safety

MDP trained on normal drivers

MDP trained on all drivers

Omniscient

POMCPOW (Ours)

Simulation results: Safety and Efficiency

[Sunberg & Kochenderfer, T-ITS 2023]

Driving among Pedestrians

[Gupta, Hayes, & Sunberg, AAMAS 2022]

Previous solution: 1-D POMDP (92s avg)

Our solution (65s avg)

State:

Vehicle physical state
Human physical state
Human intention

Target Tracking Among Pedestrians

State:

Vehicle physical state
Human physical state
Human intention
Target location

Max Likelihood Motion-Planning-Based

POMDP

Example 2: Meteorology

State: physical state of aircraft, weather
Belief: Weighting over several forecast ensemble members
Action: flight direction, drifter deploy
Reward: Terminal reward for correct weather prediction

Example 2: Tornado Prediction

Example 3: Drone Search and Rescue

State:

Location of Drone
Location of Human

Baseline

Our POMDP Planner

[Ray, Laouar, Sunberg, & Ahmed, ICRA 2023]

Drone Search and Rescue

[Ray, Laouar, Sunberg, & Ahmed, ICRA 2023]

Part IV: Multiple Agents

Partially Observable Markov Decision Process (POMDP)

\(\mathcal{S}\) - State space
\(T:\mathcal{S}\times \mathcal{A} \times\mathcal{S} \to \mathbb{R}\) - Transition probability distribution
\(\mathcal{A}\) - Action space
\(R:\mathcal{S}\times \mathcal{A} \to \mathbb{R}\) - Reward
\(\mathcal{O}\) - Observation space
\(Z:\mathcal{S} \times \mathcal{A}\times \mathcal{S} \times \mathcal{O} \to \mathbb{R}\) - Observation probability distribution

Aleatory

Epistemic (Static)

Epistemic (Dynamic)

Partially Observable Stochastic Game (POSG)

Aleatory

Epistemic (Static)

Epistemic (Dynamic)

Strategic

\(\mathcal{S}\) - State space
\(T(s' \mid s, \bm{a})\) - Transition probability distribution
\(\mathcal{A}^i, \, i \in 1..k\) - Action spaces
\(R^i(s, \bm{a})\) - Reward function (cooperative, opposing, or somewhere in between)
\(\mathcal{O}^i, \, i \in 1..k\) - Observation spaces
\(Z(o^i \mid \bm{a}, s')\) - Observation probability distributions

POSG Example: Missile Defense

POMDP Solution:

Assume a distribution for the missile's actions
Update belief according to this distribution
Use a POMDP planner to find the best defensive action

Need some Game Theory!

Nash equilibrium: All players play a best response to the other players

A shrewd missile operator will use different actions, invalidating our belief

Simplified Missile Defense Game

Attacker

Defender

Down

-1, 1

1, -1

-1, 1

Collision

No Pure Nash Equilibrium!

Need a broader solution concept: Mixed Nash equilibrium (includes deceptive behavior like bluffing)

Nash equilibrium: All players play a best response to the other players

Tabletop Game 1: Go

Improvements Needed

Simultaneous Play
State Uncertainty

Policy Network

Value Network

1. Simultaneous Play

1. Exploration

2. Selection

20 steps of regret matching on \(\tilde{A}\)

3. Networks

Policy trained to match solution to \(\bar{A}\)

Value distribution trained on sim outcomes

[Becker & Sunberg, AMOS 2025]

1. Simultaneous Play:

Space Domain Awareness

[Becker & Sunberg, AMOS 2025]

What about state uncertainty?

Tabletop Game 2: Poker

Image: Russel & Norvig, AI, a modern approach

P1: A

P1: K

P2: A

P2: K

2. State Uncertainty in Games

[Becker & Sunberg, AAMAS 2025 Short Paper]

2. State Uncertainty in Games

[Becker & Sunberg, AAMAS 2025 Short Paper]

Open Source Software!

[Krusniak et al. AAMAS 2026]

Decisions.jl

Arbitrary Dynamic Decision Networks

POMDPs.jl

using POMDPs, QuickPOMDPs, POMDPTools, QMDP

m = QuickPOMDP(
    states = ["left", "right"],
    actions = ["left", "right", "listen"],
    observations = ["left", "right"],
    initialstate = Uniform(["left", "right"]),
    discount = 0.95,

    transition = function (s, a)
        if a == "listen"
            return Deterministic(s)
        else # a door is opened
            return Uniform(["left", "right"]) # reset
        end
    end,

    observation = function (s, a, sp)
        if a == "listen"
            if sp == "left"
                return SparseCat(["left", "right"], [0.85, 0.15])
            else
                return SparseCat(["right", "left"], [0.85, 0.15])
            end
        else
            return Uniform(["left", "right"])
        end
    end,

    reward = function (s, a)
        if a == "listen"
            return -1.0
        elseif s == a # the tiger was found
            return -100.0
        else # the tiger was escaped
            return 10.0
        end
    end
)

solver = QMDPSolver()
policy = solve(solver, m)

Thank You!

www.cu-adcl.org

Funding orgs: (all opinions are my own)

Part V: Open Source Research Software

Good Examples

Open AI Gym interface
OMPL
ROS

Challenges for POMDP Software

There is a huge variety of
- Problems
  - Continuous/Discrete
  - Fully/Partially Observable
  - Generative/Explicit
  - Simple/Complex
- Solvers
  - Online/Offline
  - Alpha Vector/Graph/Tree
  - Exact/Approximate
- Domain-specific heuristics
POMDPs are computationally difficult.

Explicit

Black Box

("Generative" in POMDP lit.)

\(s,a\)

\(s', o, r\)

Previous C++ framework: APPL

"At the moment, the three packages are independent. Maybe one day they will be merged in a single coherent framework."

Open Source Research Software

Performant
Flexible and Composable
Free and Open
Easy for a wide range of people to use (for homework)
Easy for a wide range of people to understand

C++

Python, C++

Python, Matlab

Python, C++

2013

We love [Matlab, Lisp, Python, Ruby, Perl, Mathematica, and C]; they are wonderful and powerful. For the work we do — scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing — each one is perfect for some aspects of the work and terrible for others. Each one is a trade-off.

We are greedy: we want more.

2012

POMDPs.jl - An interface for defining and solving MDPs and POMDPs in Julia

Mountain Car

partially_observable_mountaincar = QuickPOMDP(
    actions = [-1., 0., 1.],
    obstype = Float64,
    discount = 0.95,
    initialstate = ImplicitDistribution(rng -> (-0.2*rand(rng), 0.0)),
    isterminal = s -> s[1] > 0.5,

    gen = function (s, a, rng)        
        x, v = s
        vp = clamp(v + a*0.001 + cos(3*x)*-0.0025, -0.07, 0.07)
        xp = x + vp
        if xp > 0.5
            r = 100.0
        else
            r = -1.0
        end
        return (sp=(xp, vp), r=r)
    end,

    observation = (a, sp) -> Normal(sp[1], 0.15)
)

using POMDPs
using QuickPOMDPs
using POMDPPolicies
using Compose
import Cairo
using POMDPGifs
import POMDPModelTools: Deterministic

mountaincar = QuickMDP(
    function (s, a, rng)        
        x, v = s
        vp = clamp(v + a*0.001 + cos(3*x)*-0.0025, -0.07, 0.07)
        xp = x + vp
        if xp > 0.5
            r = 100.0
        else
            r = -1.0
        end
        return (sp=(xp, vp), r=r)
    end,
    actions = [-1., 0., 1.],
    initialstate = Deterministic((-0.5, 0.0)),
    discount = 0.95,
    isterminal = s -> s[1] > 0.5,

    render = function (step)
        cx = step.s[1]
        cy = 0.45*sin(3*cx)+0.5
        car = (context(), circle(cx, cy+0.035, 0.035), fill("blue"))
        track = (context(), line([(x, 0.45*sin(3*x)+0.5) for x in -1.2:0.01:0.6]), stroke("black"))
        goal = (context(), star(0.5, 1.0, -0.035, 5), fill("gold"), stroke("black"))
        bg = (context(), rectangle(), fill("white"))
        ctx = context(0.7, 0.05, 0.6, 0.9, mirror=Mirror(0, 0, 0.5))
        return compose(context(), (ctx, car, track, goal), bg)
    end
)

energize = FunctionPolicy(s->s[2] < 0.0 ? -1.0 : 1.0)
makegif(mountaincar, energize; filename="out.gif", fps=20)