A novel model arbitrates between planning and habitual control for efficient decision-making.
problem Balancing flexibility and efficiency in decision-making systems.
method Introduces an arbitrator that switches between planning and habitual control systems.
result The model learns kinematics quickly and adapts to changing environments.
Dan Lovallo and Daniel Kahneman must be commended for their clear identification of causes and cures to the planning fallacy in "Delusions of Success: How Optimism Undermines Executives' Decisions" (HBR July 2003). Their look at overoptimism, anchoring, competitor neglect, and the outside view in forecasting is highly …
Algorithm learns multiple tasks with minimal planning, achieving near-optimal performance.
problem Learning multiple tasks efficiently in a reinforcement learning setting.
method UCB Lifelong Value Distillation (UCBlvd) algorithm with structural assumption for shared exploration.
result Sublinear regret bound of i l d e O ( ( d 3 + d ′ d ) H 4 K ) ilde{\mathcal{O}}(\sqrt{(d^3+d^\prime d)H^4K}) i l d e O ( ( d 3 + d ′ d ) H 4 K ) with O ( d H log ( K ) ) \mathcal{O}(dH\log(K)) O ( d H log ( K )) planning calls. Adapts IRL for dual-system agents, correcting goal inference errors.
problem Inferring goals from dual-system decision-making behaviors.
method Generalized dual-system framework, optimal plan computation, adapted IRL algorithm.
result Correct goal inference for dual-system agents improves overall utility.
New algorithm learns satisficing behaviors more efficiently in complex environments.
problem Intractability of optimal exploration in complex environments.
method Extends a deep reinforcement learning agent to learn satisficing policies without model-based planning.
result Demonstrates efficient learning of satisficing behaviors and optimal behaviors when feasible.
New algorithm syncs multi-player bandits by deliberately causing collisions.
problem Stochastic multiplayer multi-armed bandit problem with collisions.
method Decentralized algorithm that exploits communication between players to share information.
result Achieves performance of centralized algorithm with negligible cost.
Novel framework evaluates deep learning models under distribution shift.
problem Evaluating robustness and generalization of deep learning models under distribution shift.
method Variational Inference, Auto-Encoding Variational Bayes, Variational Gaussian Mixture Model, Wasserstein distance.
result Popular CNN architectures and Bayesian CNN models robustness and generalization under distribution shift are assessed.
DPN combines model-based and model-free reinforcement learning for efficient planning.
problem Efficiently plan actions in reinforcement learning environments.
method Combines model-based and model-free reinforcement learning, dynamically constructing plans using a learned state-transition model.
result Reduces the number of state transitions during planning by up to 96%, improving data efficiency and performance.
Improves RL planning by proposing sub-goals hierarchically.
problem Sequential planning assumption in RL.
method Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS).
result Improves navigation and control tasks.
New method designs fairer transport plans with uncertainty.
problem Designing fair and balanced mass transport plans.
method Hierarchical fully probabilistic design (HFPD) for transport plans.
result Optimal hyperprior for transport plans with uncertain marginals.
MPNet uses neural networks for efficient motion planning.
problem Exponential increase in computational complexity with motion planning problem dimensionality.
method MPNet encodes workspaces from point cloud measurements and generates collision-free paths.
result MPNet is computationally efficient and generalizes to unseen environments.
Study on human planning and re-planning in unknown stochastic environments.
problem Understanding how humans adjust plans in unfamiliar environments.
method Grid world task, 12 different models, model-based reinforcement learning approach.
result Model-based reinforcement learning approach best explains human re-planning behavior.
Unified framework integrates symbolic planning and HRL for robust decision-making.
problem Combining reinforcement learning and symbolic planning for robust decision-making in dynamic environments.
method Integrates symbolic planning with hierarchical reinforcement learning to guide task execution and improve planning.
result Unified framework leads to rapid policy search and robust symbolic plans in complex domains.
We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then trans…
Generative model learns goal-directed visual plans from raw data.
problem Learning goal-directed visual plans from high-dimensional observations.
method Combines representation learning and planning, maximizing mutual information.
result Generative model learns low-dimensional representation explaining causal data.
First model-based planner learns to construct, evaluate, and execute plans.
problem Challenges in model-based planning, especially in constructing plans.
method Imagination-based Planner that learns to construct, evaluate, and execute plans.
result Can learn to solve challenging continuous control problems and elaborate planning strategies.
Generative adversarial networks improve radiation therapy planning.
problem Creating optimal radiation therapy plans.
method Using a GAN to predict 3D dose distributions directly.
result Significantly outperforms previous methods in clinical satisfaction and metrics.
Study integrates reliability constraints into generation planning models.
problem Challenges in integrating reliability constraints with generation planning models.
method Leverages a weighted oblique decision tree (WODT) technique to embed reliability verification constraints.
result Demonstrates effectiveness in achieving reliable and optimal planning solutions.
Information planning speeds up text data learning with fewer examples.
problem Learning with limited training examples for text data.
method Entropy and mutual information-based information planning for three supervised models.
result Information planning accelerates learning for text data.
New approach improves black-box planning efficiency by discovering focused macros.
problem Difficulty of deterministic planning increases exponentially with depth.
method Discovering macro-actions with focused effects to improve goal-count heuristics.
result Focused macros dramatically improve black-box planning efficiency.
UPNs embed planning within a goal-directed policy for effective visuomotor control.
problem Learning abstract representations for visuomotor control and generalization.
method Differentiable planning within a latent space, gradient descent trajectory optimization, end-to-end learning of representations.
result UPNs can transfer visuomotor planning strategies across robots with different morphologies and actuation capabilities.
Study motion planning for points avoiding obstacles in a plane.
problem Avoiding collisions for multiple points in a plane with unknown obstacles.
method Algebraic and topological tools for motion planning.
result New topological complexity for planar motion planning.
Selective planning with imperfect models reduces harmful effects of model inadequacy.
problem Harmful effects of using an imperfect model in reinforcement learning.
method Selective planning with heteroscedastic regression to estimate predictive uncertainty from model inadequacy.
result Effective selective planning requires considering both parameter uncertainty and model inadequacy.
CoMPNetX uses neural networks to efficiently solve constrained motion planning problems.
problem Finding collision-free paths on constraint manifolds efficiently.
method Neural generator and discriminator with neural gradients-based projection operator.
result CoMPNetX finds path solutions with high success rates and lower computation times.
This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…
Projective simulation outperforms standard reinforcement learning in navigation tasks.
problem Benchmarking projective simulation in navigation problems.
method Projective simulation model applied to grid world and mountain car problems, compared to Q-learning and SARSA.
result Projective simulation outperforms standard reinforcement learning in terms of parameter simplicity and computational efficiency.
New method combines heuristics and search techniques to speed up cooperative planning for autonomous vehicles.
problem Efficient cooperative planning for autonomous vehicles in complex traffic scenarios.
method Combining learned heuristics with Monte Carlo Tree Search (MCTS) to guide search towards promising actions.
result Better solutions at lower computational costs achieved through accelerated planning.
This paper presents a unifying framework for reinforcement learning and planning.
problem Sequential decision making in AI, formalized as MDP optimization.
method A unifying algorithmic framework (FRAP) for reinforcement learning and planning.
result Identifies common dimensions in MDP planning and learning algorithms.
A planning approach learns skills from interactions, balancing exploration and exploitation.
problem Learning robust high-level skills in noisy environments with unknown pre-conditions.
method Formulates skills as high-level policies, learns plans via bandit problems, balances exploration and exploitation.
result A planner capable of learning robust high-level skills in high-dimensional state spaces.
Paper evaluates how forecast errors affect optimal utilisation in production planning.
problem Forecast errors impact optimal utilisation in production planning.
method Simulation and mixed integer programming for stochastic demand.
result Forecast errors significantly affect optimal costs in production planning.
Survey of integrating planning and learning in model-based reinforcement learning.
problem Sequential decision making in AI, formalized as MDP optimization.
method Systematic coverage of dynamics model learning and planning-learning integration.
result Broad conceptual overview of model-based reinforcement learning.
New method uses autoregressive models for dynamic planning.
problem Planning in dynamic environments with moving obstacles and goals.
method Conditional autoregressive generative models in a discrete latent space.
result Method nearly matches true environment performance for planning.
New approach for obstacle avoidance in robotics using learned representations.
problem Challenges in sensor-based motion planning for new and dynamic environments.
method Proposes a new obstacle representation using PointNet architecture trained jointly with policies for obstacle avoidance.
result Significant improvements in accuracy and efficiency compared to state of the art.
This work clarifies the role of inference types in planning.
problem Lack of consistency in using inference types for planning.
method Variational framework and loopy belief propagation.
result All inference types correspond to different weights in variational problems.
This work tackles long-term visual planning by goal-conditioned hierarchical predictors.
problem Current learning approaches fail on long-horizon tasks due to lack of goal information and coarse-to-fine planning.
method Formulate goal-conditioned predictors (GCPs) and hierarchical models to predict trajectories between observations.
result GCPs enable effective long-term planning with much longer horizons than before.
Improved motion planning for dynamic environments using RL.
problem Efficient motion planning in changing environments.
method Deep reinforcement learning (DDPG) tailored for motion planning.
result DDPG-MP significantly improves motion planning accuracy.
We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natura…
PlaNet learns latent dynamics from images for better planning in unknown environments.
problem Leveraging planning in unknown environments with accurate dynamics models.
method Deep Planning Network (PlaNet) learns dynamics from images using latent space and multi-step variational inference.
result PlaNet achieves high performance in continuous control tasks with contact dynamics and sparse rewards.
Study examines how mergers and acquisitions affect Indian banks' financial performance and capital structure.
problem Impact of mergers and acquisitions on Indian banks' financial performance and capital structure.
method Statistical analysis using paired t-test on selected banks' annual reports.
result Mergers and acquisitions significantly impact financial performance and capital structure of Indian banks.
This work defines a complexity measure for BAMDP planning and introduces state abstraction for more efficient approximate planning.
problem The computational intractability of exact BAMDP planning solutions.
method Define a complexity measure for BAMDP planning, introduce state abstraction, and develop an approximate planning algorithm.
result Introduces a computationally tractable approximate planning algorithm using state abstraction.
We show that deliberately introducing a nested simulation stage can lead to significant variance reductions when comparing two stopping times by Monte Carlo. We derive the optimal number of nested simulations and prove that the algorithm is remarkably robust to misspecifications of this number. The method is applied to…
Model-free RL agents can learn effective planning without explicit model.
problem Challenging domains with combinatorial complexity.
method Model-free RL with inductive bias in neural networks.
result Model-free RL can exhibit planning characteristics without explicit model.
Hierarchical Foresight improves robot vision tasks by planning long-term goals.
problem Compounding uncertainty and scalability issues in long horizon video prediction.
method Subgoal generation and planning using hierarchical visual foresight (HVF).
result Achieves nearly 200% performance improvement in vision-based manipulation tasks.
PALM learns abstract models for efficient planning and task transfer.
problem Efficiently learning and transferring hierarchical models for planning.
method PALM uses a new formal structure (L-AMDP) to learn independent, modular models at multiple levels of abstraction.
result PALM integrates planning and execution, facilitating rapid learning of abstract models.
The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We …
Greedy policies in model-based RL achieve tight regret bounds without full planning.
problem Achieving efficient RL algorithms in MDP settings.
method Using greedy policies for 1-step planning in model-based RL.
result Greedy policies achieve i l d e O ( H S A T ) ilde{\mathcal{O}}(\sqrt{HSAT}) i l d e O ( H S A T ) regret bounds. A planning mechanism improves sequence-to-sequence model performance.
problem Improving sequence-to-sequence model performance for tasks like translation and graph analysis.
method Developed a model that plans ahead using attention and differentiable operations.
result Outperforms strong baselines on character-level translation, graph analysis, and question generation.
CoTj improves diffusion model quality and stability via graph planning.
problem Rigidity in diffusion models due to high-dimensional state space.
method Chain-of-Trajectories (CoTj) framework using Diffusion DNA for graph planning.
result CoTj discovers context-aware trajectories improving output quality and stability.