Comprehensive Roadmap for Learning Planning and Decision Making

Welcome to your complete guide for mastering Planning and Decision Making in Artificial Intelligence! This comprehensive roadmap provides a structured learning path from foundational concepts to cutting-edge research, complete with practical projects and essential resources.

1. Structured Learning Path

Phase 1: Foundations (2-3 months)

1.1 Mathematical Prerequisites

Linear Algebra: Vectors, matrices, eigenvalues, matrix operations
Probability Theory: Conditional probability, Bayes' theorem, distributions
Optimization: Convex optimization, gradient descent, linear programming
Graph Theory: Trees, directed graphs, search algorithms
Calculus: Derivatives, dynamic programming principles

1.2 Classical AI Search

Uninformed Search: BFS, DFS, uniform-cost search, iterative deepening
Informed Search: A*, greedy best-first, heuristic functions
Adversarial Search: Minimax, alpha-beta pruning, expectimax
Constraint Satisfaction Problems (CSPs): Backtracking, arc consistency, local search

1.3 Logic and Knowledge Representation

Propositional Logic: SAT solvers, resolution
First-Order Logic: Inference, unification
Planning Domain Definition Language (PDDL): Basic syntax and semantics

Phase 2: Classical Planning (2-3 months)

2.1 STRIPS and Classical Planning

State-space planning
Plan-space planning
Graph-based planning (Planning graphs, GraphPlan)
Heuristic search planning (Fast-Forward, Fast-Downward)

2.2 Advanced Planning Paradigms

Hierarchical Task Network (HTN) Planning: Task decomposition, SHOP2
Temporal Planning: Durative actions, temporal constraints
Planning with Uncertainty: Conformant planning, contingent planning
Partial-Order Planning: Least-commitment strategy

2.3 Domain-Independent Planning

Heuristics extraction
Landmarks and pattern databases
Abstraction techniques

Phase 3: Decision Making Under Uncertainty (3-4 months)

3.1 Markov Decision Processes (MDPs)

Fundamentals: States, actions, transitions, rewards, policies
Value Iteration: Bellman equations, convergence
Policy Iteration: Policy evaluation and improvement
Linear Programming formulation for MDPs

3.2 Reinforcement Learning (RL)

Model-Free Methods

Temporal Difference (TD) learning
Q-Learning, SARSA
Function approximation

Policy Gradient Methods

REINFORCE algorithm
Actor-Critic methods
Proximal Policy Optimization (PPO)
Trust Region Policy Optimization (TRPO)

Deep RL

Deep Q-Networks (DQN)
Double DQN, Dueling DQN
Deep Deterministic Policy Gradient (DDPG)
Soft Actor-Critic (SAC)

3.3 Partially Observable MDPs (POMDPs)

Belief states
Value iteration for POMDPs
Point-based value iteration
Monte Carlo tree search for POMDPs

Phase 4: Multi-Agent Systems (2-3 months)

4.1 Game Theory Foundations

Normal-form games, Nash equilibrium
Extensive-form games
Repeated games, Folk theorems
Mechanism design basics

4.2 Multi-Agent Planning and Learning

Cooperative planning
Multi-agent reinforcement learning (MARL)
Communication protocols
Coordination mechanisms
Decentralized POMDPs (Dec-POMDPs)

4.3 Auction and Voting Theory

Auction mechanisms
Social choice theory
Coalition formation

Phase 5: Advanced Topics (3-4 months)

5.1 Monte Carlo Methods

Monte Carlo Tree Search (MCTS)
Upper Confidence Bounds for Trees (UCT)
Applications: AlphaGo, AlphaZero

5.2 Probabilistic Planning

Probabilistic PDDL
Stochastic shortest path problems
Risk-sensitive planning

5.3 Learning for Planning

Learning domain models
Transfer learning in planning
Meta-learning for decision making
Imitation learning and inverse reinforcement learning

5.4 Explainable Planning and Decision Making

Interpretable policies
Contrastive explanations
Plan visualization and communication

2. Major Algorithms, Techniques, and Tools

Core Algorithms

Search Algorithms

A* and variants (IDA*, SMA*, RBFS*)
Dijkstra's algorithm
Bidirectional search
Jump Point Search (for grids)

Planning Algorithms

GraphPlan
Fast-Forward (FF)
Fast-Downward (FD)
SHOP2, SIPE-2 (HTN)
Metric-FF (metric planning)
TFD/ITSAT (temporal planning)
Contingent-FF (contingent planning)

MDP/RL Algorithms

Value Iteration, Policy Iteration
Q-Learning, SARSA, Expected SARSA
DQN, Rainbow DQN
A3C (Asynchronous Advantage Actor-Critic)
PPO, TRPO
SAC, TD3
Model-based RL: Dyna-Q, PILCO, PETS
World Models

POMDP Algorithms

PBVI (Point-Based Value Iteration)
PERSEUS
SARSOP
POMCP (Partially Observable Monte Carlo Planning)
DESPOT (Determinized Sparse Partially Observable Tree)

Multi-Agent Algorithms

Nash-Q Learning
Friend-or-Foe Q-Learning
QMIX, QTRAN
MADDPG (Multi-Agent DDPG)
CommNet, TarMAC (communication-based)

Monte Carlo Methods

UCT (Upper Confidence bounds applied to Trees)
AlphaGo/AlphaZero architecture
MuZero
Counterfactual Regret Minimization (CFR)

Key Techniques

Heuristic Design: Admissibility, consistency, pattern databases, abstractions
Pruning: Alpha-beta, branch and bound
Decomposition: Hierarchical planning, options framework
Abstraction: State aggregation, hierarchical representations
Transfer Learning: Domain adaptation, curriculum learning
Exploration Strategies: ε-greedy, Boltzmann, UCB, Thompson sampling
Credit Assignment: Eligibility traces, n-step returns
Function Approximation: Linear, neural networks, tile coding
Reward Shaping: Potential-based shaping, intrinsic motivation

Essential Tools and Frameworks

Planning Tools

Fast-Downward: State-of-the-art classical planner
PDDL Editors: Planning.domains, Visual Studio Code extensions
VAL: Plan validator for PDDL
Madagascar: SAT-based planner
OPTIC: Temporal planner

RL and MDP Tools

OpenAI Gym/Gymnasium: Standard RL environments
Stable-Baselines3: Reliable RL implementations
RLlib (Ray): Scalable RL library
TF-Agents: TensorFlow-based RL
Acme (DeepMind): Research RL framework
PettingZoo: Multi-agent environments

POMDP Tools

POMDP .jl: Julia-based POMDP toolkit
AI-Toolbox: C++ POMDP/MDP library
TAPIR: POMDP solver toolkit

General Purpose

Python Libraries: NumPy, SciPy, NetworkX
Deep Learning: PyTorch, TensorFlow, JAX
Optimization: CVXPY, Gurobi, CPLEX
Simulation: MuJoCo, PyBullet, Unity ML-Agents
Visualization: Matplotlib, Plotly, TensorBoard

3. Cutting-Edge Developments

Foundation Models for Planning and Decision Making

Large Language Models (LLMs) as planners and reasoners
Prompt engineering for planning tasks
Code generation for automated planning
Multimodal planning with vision-language models
Recent Work: SayCan, Voyager, Planner-Actor-Reporter

Offline Reinforcement Learning

Learning from fixed datasets without environment interaction
Conservative Q-Learning (CQL)
Implicit Q-Learning (IQL)
Decision Transformer architecture
Applications in robotics and healthcare

Model-Based RL Renaissance

Learned world models (Dreamer v3, IRIS)
Planning with learned models
Model-based policy optimization
Hybrid approaches combining model-free and model-based

Safe and Constrained Decision Making

Constrained MDPs and safe RL
Shielding and runtime verification
Risk-sensitive planning
Robust planning under uncertainty
Applications: Autonomous vehicles, medical treatment planning

Neuro-Symbolic Planning

Integration of symbolic reasoning with neural networks
Differentiable planning modules
Neural theorem proving
Learning symbolic representations

Multi-Task and Continual Learning

Lifelong learning for agents
Zero-shot and few-shot planning
Task composition and generalization
Meta-reinforcement learning advances

Human-AI Collaboration

Interactive task learning
Preference learning and alignment
Explainable AI for planning
Co-design of human-robot teams

Quantum Planning and Optimization

Quantum approximate optimization algorithm (QAOA)
Variational quantum eigensolvers for planning
Quantum-inspired classical algorithms

Emerging Applications

Molecular Design: Planning synthesis pathways
Climate and Sustainability: Long-horizon planning for environmental systems
Personalized Medicine: Treatment planning as sequential decision making
Smart Cities: Traffic optimization, resource allocation

4. Project Ideas (Beginner to Advanced)

Beginner Projects

Project 1: Path Planning Visualizer

Description: Implement A*, Dijkstra, and BFS for grid-based path finding with interactive visualization showing algorithm progression.

Skills: Search algorithms, data structures, visualization

Features: Compare performance metrics (nodes expanded, path cost)

Project 2: Sliding Puzzle Solver

Description: Implement 8-puzzle/15-puzzle solver using A* with multiple heuristics.

Skills: Heuristic search, state space representation

Features: Compare Manhattan distance vs. misplaced tiles heuristic, implement IDA* for memory efficiency

Project 3: Tic-Tac-Toe with Minimax

Description: Implement minimax algorithm with alpha-beta pruning and AI opponent with different difficulty levels.

Skills: Game trees, adversarial search

Features: Extend to Connect-4 or other simple games

Project 4: Simple GridWorld MDP Solver

Description: Implement value iteration and policy iteration for a grid world environment.

Skills: MDPs, dynamic programming

Features: Visualize value functions and optimal policies, experiment with different reward structures

Intermediate Projects

Project 5: Autonomous Warehouse Robot

Description: Use PDDL to model warehouse operations and implement planner for multi-robot task allocation.

Skills: Classical planning, PDDL, temporal reasoning

Features: Handle temporal constraints (charging, task deadlines)

Project 6: Q-Learning for Game Playing

Description: Implement tabular Q-learning for Atari-style games with experience replay and target networks.

Skills: Reinforcement learning, function approximation

Features: Compare with DQN implementation

Project 7: Dialogue System with MDPs

Description: Model conversation as MDP/POMDP and implement policy for optimal dialogue management.

Skills: POMDPs, natural language processing

Features: Handle uncertainty in user intent

Project 8: Multi-Armed Bandit Algorithms

Description: Implement ε-greedy, UCB, Thompson sampling and compare regret bounds empirically.

Skills: Exploration-exploitation, online learning

Features: Apply to recommendation system or A/B testing scenario

Project 9: Monte Carlo Tree Search for Board Games

Description: Implement MCTS with UCT for chess or Go variants.

Skills: MCTS, simulation-based planning

Features: Add domain-specific enhancements, compare with minimax approaches

Advanced Projects

Project 10: Deep RL for Robotic Control

Description: Use PPO or SAC for continuous control tasks (MuJoCo) with sim-to-real transfer techniques.

Skills: Deep RL, robotics, transfer learning

Features: Add curriculum learning for complex behaviors

Project 11: Hierarchical Planning for Long-Horizon Tasks

Description: Implement HTN planner with learned skill library using options framework for temporal abstraction.

Skills: Hierarchical planning, skill learning

Features: Apply to cooking recipes or assembly tasks

Project 12: Multi-Agent Coordination

Description: Implement QMIX or MADDPG for cooperative tasks and test on StarCraft Multi-Agent Challenge.

Skills: Multi-agent RL, coordination

Features: Add communication mechanisms

Project 13: Offline RL from Demonstrations

Description: Implement CQL or behavioral cloning with dataset and apply to real-world dataset.

Skills: Offline RL, imitation learning

Features: Compare online fine-tuning vs. pure offline

Project 14: Safe RL with Constraints

Description: Implement constrained policy optimization with safety shields or backup policies.

Skills: Safe RL, constrained optimization

Features: Test on safety-critical scenarios (autonomous driving sim)

Project 15: LLM-Based Planning Agent

Description: Use GPT-4 or similar for task planning with plan verification and correction.

Skills: LLMs, neuro-symbolic integration, prompt engineering

Features: Combine with classical planner for guaranteed correctness

Expert Projects

Project 16: Learned World Model for Planning

Description: Implement Dreamer or similar model-based RL with world model training on visual observations.

Skills: Model-based RL, representation learning

Features: Use imagination for planning in latent space

Project 17: POMDP Solver for Real-World Problem

Description: Model autonomous drone navigation as POMDP and implement online POMDP solver.

Skills: POMDPs, approximate inference, robotics

Features: Handle continuous state/observation spaces

Project 18: Meta-RL for Rapid Adaptation

Description: Implement MAML or RL² for few-shot learning and test on distribution of related tasks.

Skills: Meta-learning, transfer learning, optimization

Features: Apply to robotics or game playing

Project 19: Explainable Planning System

Description: Build planner that generates natural language explanations with contrastive explanation generation.

Skills: XAI, NLP, human-AI interaction

Features: Create interactive interface for plan exploration

Project 20: Research Implementation

Description: Reproduce recent paper from top conferences (ICAPS, NeurIPS, AAAI) and extend with novel contributions.

Skills: Research methodology, experimental design

Features: Benchmark on standard datasets

Learning Resources

Online Courses

Stanford CS221: Artificial Intelligence
UC Berkeley CS188: Introduction to AI
DeepMind x UCL RL Course
MIT 6.034: Artificial Intelligence

Key Conferences to Follow

ICAPS (International Conference on Automated Planning and Scheduling)
NeurIPS, ICML (Machine Learning)
AAAI, IJCAI (General AI)
AAMAS (Multi-agent systems)

This roadmap provides a comprehensive path through planning and decision making. Start with foundations, build practical projects alongside theory, and gradually progress to cutting-edge research topics. Focus on implementing algorithms yourself rather than just using libraries—this builds deep understanding that's essential for research and advanced applications.