World Models Handbook

Develops the topic through a minimal latent dynamics model, prediction, reconstruction, and rollout, planning with imagined trajectories, and the complete training and evaluation loop.

Develops the topic through operational and philosophical definitions, models of observations, states, dynamics, and agents, prediction versus simulation, and boundaries with generative models and simulators.

Develops the topic through state sufficiency, counterfactual prediction, action conditioning, and decision usefulness and closed-loop evidence.

Develops the topic through explicit and implicit models, pixel, token, latent, object, and symbolic state, deterministic and stochastic dynamics, and task-specific and general-purpose models.

Develops the topic through internal models in neuroscience and cognitive science, cybernetics and control, AI planning and model-based learning, and modern generative and foundation models.

Develops the topic through random variables and conditional distributions, state transitions and trajectories, fixed points, stability, and attractors, and information, compression, and sufficient statistics.

Develops the topic through MDPs, POMDPs, and controlled dynamical systems, states, actions, observations, rewards, and horizons, Markov assumptions and their failure, and policies, returns, and value functions.

Develops the topic through Bayes filters and recursive state estimation, Kalman and extended Kalman filters, particle filters, and learned belief-state updates.

Develops the topic through identifiability and excitation, parametric and nonparametric dynamics, subspace and prediction-error methods, and neural system identification from trajectories.

Develops the topic through state-space search, dynamic programming and Bellman equations, linear-quadratic control, and trajectory optimization and constraints.

Develops the topic through probabilistic control objectives, optimality variables and graphical models, free energy and expected free energy, and connections and distinctions between frameworks.

Develops the topic through learning versus using a dynamics model, planning, policy optimization, and value learning, sample efficiency and model bias, and Dyna-style integration of real and imagined data.

Develops the topic through vision, audio, language, touch, and proprioception, sensor calibration and synchronization, missing, delayed, and noisy observations, and multimodal fusion and alignment.

Develops the topic through predictive and control-sufficient state, bisimulation and behavioral equivalence, minimal state and nuisance invariance, and task-aware representation learning.

Develops the topic through autoencoders and variational inference, discrete and continuous latents, posterior collapse and information bottlenecks, and latent geometry and semantic organization.

Develops the topic through recurrent memory and temporal context, short-term versus long-term state, episodic and external memory, and non-Markov environments and state aliasing.

Develops the topic through object discovery and slot representations, relations, graphs, and message passing, identity, permanence, and tracking, and interaction networks and compositional scenes.

Develops the topic through agent-environment boundaries, body schemas and kinematic state, capabilities, constraints, and affordances, and modeling other agents and theory of mind.

Develops the topic through coordinate systems and camera geometry, depth, pose, and scene flow, SLAM, occupancy, and semantic maps, and neural fields and dynamic 3D representations.

Develops the topic through rigid and deformable dynamics, contact, friction, and collision, conservation laws and symmetries, and physics priors and constraint enforcement.

Develops the topic through structural causal models, interventions and counterfactuals, causal representation learning, and confounding, invariance, and transportability.

Develops the topic through differentiable simulators, graph-based physical models, neural differential equations, and operator learning for fields and PDEs.

Develops the topic through aleatoric and epistemic uncertainty, multimodal futures, ensembles, Bayesian models, and distributions, and calibration and uncertainty propagation.

Develops the topic through factorized mechanisms, recombining objects, relations, and skills, out-of-distribution composition, and measuring systematic generalization.

Develops the topic through deterministic and stochastic hidden state, recurrent state-space models, posterior inference and prior dynamics, and multi-step latent rollout.

Develops the topic through attention over trajectories, causal sequence modeling, long-context memory and efficient recurrence, and transformer and state-space hybrids.

Develops the topic through visual, action, and multimodal tokenizers, next-token dynamics, spatiotemporal token layouts, and exposure bias and long-horizon generation.

Develops the topic through conditional denoising dynamics, diffusion over states and trajectories, flow matching and continuous-time generation, and controllability, latency, and sampling tradeoffs.

Develops the topic through predicting representations instead of pixels, joint-embedding predictive architectures, energy functions and compatibility learning, and collapse prevention and target encoders.

Develops the topic through multiple temporal and spatial scales, neural-symbolic and simulator hybrids, shared state across modalities, and modular experts and routing.

Develops the topic through reconstruction and likelihood objectives, contrastive and masked prediction, multi-step and overshooting losses, and balancing representation, dynamics, and reward losses.

Develops the topic through action-conditioned transitions, inverse dynamics and controllability, learning actions from unlabeled video, and latent action alignment with real controls.

Develops the topic through agent-collected trajectories, observational video and internet data, coverage, behavior policy, and interventions, and combining passive and embodied experience.

Develops the topic through trajectory quality and diversity, deduplication, filtering, and balancing, simulation and procedural generation, and data mixtures, provenance, and contamination.

Develops the topic through batch and streaming updates, distribution shift and replay, catastrophic forgetting, and continual adaptation without unsafe drift.

Develops the topic through scaling data, parameters, context, and compute, task adaptation and instruction conditioning, preference and control-oriented post-training, and transfer across embodiments and domains.

Develops the topic through random shooting and cross-entropy methods, model predictive control, receding horizons and replanning, and constraints, warm starts, and real-time budgets.

Develops the topic through backpropagation through dynamics, gradient-based action optimization, differentiable value and cost functions, and stability under long rollout gradients.

Develops the topic through tree search and Monte Carlo tree search, planning under partial observability, belief trees and information gathering, and learned search guidance.

Develops the topic through latent imagined rollouts, actor-critic learning inside a model, value expansion and synthetic experience, and regularizing policies against model errors.

Develops the topic through actions that improve both knowledge and reward, information gain and uncertainty reduction, curiosity and intrinsic motivation, and exploration with safety constraints.

Develops the topic through skills, options, and temporal abstraction, symbolic abstractions and task planning, language-conditioned goals and plans, and coordination, opponents, and nested beliefs.

Develops the topic through probabilistic dynamics and data efficiency, trajectory sampling with ensembles, uncertainty-aware model predictive control, and short model rollouts for policy optimization.

Develops the topic through variational latent dynamics, controller learning in dreams, video prediction for Atari, and latent planning with learned state-space models.

Develops the topic through recurrent state-space representations, latent imagination and actor-critic learning, DreamerV2 and discrete latents, and DreamerV3 and cross-domain scaling.

Develops the topic through representation, dynamics, and prediction networks, planning without reconstructing observations, value equivalence and task sufficiency, and search targets and reanalysis.

Develops the topic through joint latent dynamics and value learning, temporal-difference model predictive control, short-horizon planning with terminal values, and TD-MPC2 and multi-task control.

Develops the topic through learning from fixed datasets, out-of-distribution actions and model uncertainty, pessimism and conservative objectives, and policy constraints and offline evaluation.

Develops the topic through discrete environment tokenization, large autoregressive dynamics models, interactive generation and action interfaces, and transfer across games and environments.

Develops the topic through frozen visual representations as state, feature-space dynamics prediction, action-conditioned latent rollouts, and generalization with pretrained encoders.

Develops the topic through video prediction and temporal coherence, prompt and action controllability, physical plausibility versus visual realism, and tests of memory, causality, and intervention.

Develops the topic through latent action discovery, playable environment generation, interactive video rollouts, and evaluation of control and consistency.

Develops the topic through physical AI data and tokenization, multimodal generative dynamics, conditioning, guardrails, and customization, and foundation-model infrastructure for simulation.

Develops the topic through joint perception, language, and control, action tokenization and robot policies, predictive models paired with action models, and embodiment transfer and closed-loop evaluation.

Develops the topic through Atari, board games, and open worlds, learned simulators and playable generation, long-horizon consistency and rules, and agents trained inside generated games.

Develops the topic through object state and contact-rich dynamics, grasping, pushing, and dexterous control, visual foresight and action-conditioned video, and task planning with learned affordances.

Develops the topic through body dynamics and terrain interaction, whole-body control, spatial memory and goal navigation, and cross-embodiment and sim-to-real transfer.

Develops the topic through bird's-eye-view and occupancy state, other-agent behavior prediction, closed-loop driving simulation, and rare events, safety, and planning.

Develops the topic through web, desktop, and code execution state, GUI and API transition models, long-horizon task simulation, and verifiable actions and environment feedback.

Develops the topic through molecular, climate, and materials dynamics, physiology and treatment response, economic and social simulation, and domain constraints, uncertainty, and misuse risk.

Develops the topic through one-step prediction, open-loop rollout, counterfactual intervention, and closed-loop planning and transfer.

Develops the topic through reconstruction and predictive accuracy, perceptual quality and distributional metrics, diversity, sharpness, and multimodality, and human judgment and learned evaluators.

Develops the topic through state probing and representation quality, object permanence and temporal consistency, physical rule and constraint tests, and interventions, counterfactuals, and causal fidelity.

Develops the topic through task return and sample efficiency, planning horizon and compute tradeoffs, model exploitation and policy ranking, and zero-shot, few-shot, and cross-domain transfer.

Develops the topic through benchmark taxonomies and coverage, train-test leakage and dataset shift, ablations, baselines, and statistical power, and reproducibility and compute reporting.

Develops the topic through latent probes and concept analysis, rollout visualization and error localization, causal interventions on representations, and distinguishing perception, dynamics, and planner failures.

Develops the topic through compounding rollout error, model bias and objective misspecification, planner exploitation and adversarial trajectories, and state aliasing, forgetting, and hallucinated dynamics.

Develops the topic through distribution shift and stress testing, calibrated uncertainty and abstention, risk-sensitive and constrained planning, and fallback policies and runtime assurance.

Develops the topic through data poisoning and model extraction, privacy in recorded environments, dual-use simulation and deceptive affordances, and documentation, access control, and accountability.

Develops the topic through distributed training and sequence parallelism, data, checkpoint, and experiment infrastructure, rollout serving, caching, and batching, and latency, memory, and energy optimization.

Develops the topic through reality gaps and domain randomization, hardware-in-the-loop validation, online monitoring and residual correction, and safe data collection and update policies.

Develops the topic through general-purpose versus embodiment-specific worlds, causal, compositional, and persistent memory, evaluation beyond visual prediction, and open research programs and practical project paths.