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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
AOP combines model-based planning with model-free learning to handle lifelong learning challenges.
problem Learning control in an online reset-free lifelong learning scenario where mistakes can compound and dynamics change.
method Adaptive Online Planning (AOP) that combines model-based planning with model-free learning, approximating uncertainty to call upon planning only when necessary.
result Achieves strong performance in lifelong learning challenges, gracefully adapting behaviors in the face of unpredictable changes.
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.
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.
End-to-end Dyna-AIL combines planning and learning for faster convergence.
problem Efficient imitation learning with minimal interactions.
method Dyna-like framework integrating model-based planning and model-free learning.
result Our approach converges to optimal policies with fewer interactions.
Regularizes model-based planning using energy-based models for efficient learning.
problem Challenges in using learned dynamics models for accurate planning.
method Regularization using energy estimates of state transitions.
result Proposes effective regularization method for planning with pre-trained dynamics models.
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiab…
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.
Neural A* uses machine learning to improve path planning efficiency.
problem Challenges in applying machine learning to search-based path planning.
method Reformulated A* search as a differentiable network coupled with a convolutional encoder.
result Neural A* outperforms state-of-the-art planners in optimality and 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.
PDSketch enables flexible robot planning by learning from domain structures.
problem Building general robots with flexible planning.
method Exploiting locality and sparsity in environmental models, PDSketch defines high-level structures for trainable neural networks.
result PDSketch automatically generates planning heuristics without additional training.
Paper introduces ML for optimal motion planning in urban traffic.
problem Variations in search efficiency due to dynamic obstacles.
method Introduces machine learning-based heuristic for optimal motion planning considering dynamic obstacles.
result Added performance consistency for real-time implementation.
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.
NEXT learns efficient paths in high dimensions using neural exploration-exploitation trees.
problem Learning efficient path planning in high-dimensional spaces.
method Neural Exploration-Exploitation Trees (NEXT) integrating neural architecture and UCB algorithm.
result NEXT achieves better sample efficiency and outperforms state-of-the-art methods.
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 method uses RL to learn abstractions for planning, improving robot navigation and manipulation tasks.
problem Planning requires suitable abstractions for states and transitions, which RL struggles with for temporally extended tasks.
method Goal-conditioned policies learned with RL are incorporated into planning, with a latent variable model representing valid states.
result Our method significantly outperforms prior work on image-based robot navigation and manipulation tasks.
Vanguard uses AI to create personalized financial plans.
problem Challenges in choosing features for complex financial planning.
method Reinforcement learning for identifying optimal savings rates.
result Trains algorithms to model financial success trajectories.
Deep reinforcement learning optimizes retrosynthetic planning for chemical synthesis.
problem Optimizing chemical synthesis plans from molecular targets to simpler starting materials.
method Deep reinforcement learning to estimate synthesis costs and values of molecules.
result Trained neural networks outperform heuristic approaches in synthesizing unfamiliar molecules.
Proposes learning latent reward model for planning from rewards.
problem Planning in high-dimensional state spaces with limited reward information.
method Directly learns a latent dynamics model from rewards, planning in latent state-space.
result Successfully learns accurate latent reward prediction model, achieving strong performance and high sample efficiency.
EBMs improve sample efficiency and generalization in RL.
problem Improving sample efficiency and generalization in reinforcement learning.
method Developed an online algorithm to train EBMs for model-based planning, leveraging their ability to infer intermediate states.
result EBMs lead to significantly better online learning and state space planning compared to feed-forward networks.
RL enhances LLM planning but introduces spurious solutions and diversity collapse.
problem Theoretical understanding of RL's benefits and limitations in LLM planning.
method Graph-based abstraction, policy gradient, Q-learning, supervised fine-tuning.
result RL's exploration is crucial for generalization, but PG suffers from diversity collapse.
PackIt creates a virtual space for testing geometric planning skills.
problem Evaluating geometric planning abilities in virtual environments.
method Developed a virtual environment, PackIt, for geometric planning tasks.
result Demonstrated the effectiveness of various methods for geometric planning.
A learning-based approach optimizes automated lane changes with mixed-integer optimization and machine learning.
problem Traditional motion planning methods are inefficient and lack generalization capability.
method Mixed-Integer Quadratic Problem (MIQP) for optimal trajectories, supervised learning for fast decision-making.
result The proposed model outperforms existing motion planning methods in optimality, efficiency, and generalization.
The paper tackles model-based RL's inaccuracy issue by dynamically adjusting planning horizons.
problem Model-based RL's failure due to model inaccuracy over long planning horizons.
method State-dependent planning horizon, learning cumulative model errors with Temporal Difference methods.
result The proposed method successfully adapts planning horizons to state-dependent model accuracy, improving policy learning efficiency.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
Robot learns new actions and plans complex tasks efficiently.
problem Efficiently plan and execute complex tasks with limited sensorimotor primitives.
method Active model learning and diverse action sampling using Gaussian process methods and adaptive sampling.
result Improved planning and execution of long-horizon tasks.
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.
The paper explores how planning with models improves credit assignment in reinforcement learning.
problem Improving credit assignment in reinforcement learning.
method Investigates the use of forward and backward models for planning in reinforcement learning.
result Establishes the relative merits and limitations of forward and backward planning mechanisms.
This work improves motion planning for quadcopters by learning and reasoning about controller performance.
problem Improving motion planning for quadcopters with safety margins and execution reliability.
method Introspective learning and reasoning to correct execution bias and improve collision checking.
result Substantial reduction in safety margins for motion actions, leading to safer execution.
PBCS combines RL and motion planning for better exploration.
problem RL algorithms struggle with versatile exploration in complex environments.
method PBCS uses motion planning to find a good trajectory, then trains RL on a curriculum derived from it.
result PBCS outperforms state-of-the-art RL algorithms in 2D maze environments.
Model-Based Offline Planning (MBOP) learns models from offline data to control systems directly.
problem Training RL policies from offline data without direct system interaction.
method Generates models from offline data and uses planning to control the system.
result Near-optimal policies found for simulated systems with minimal real-time interaction.
Paper proposes Imitative Models combining IL and planning for flexible goal achievement.
problem Difficult to direct IL to arbitrary goals and specify reward functions for goal-directed planning.
method Imitative Models are probabilistic predictive models that plan interpretable expert-like trajectories.
result Imitative Models outperform IL and planning approaches in a dynamic autonomous driving task.
A novel pedestrian path-planning model using reinforcement learning.
problem Challenges in replicating natural pedestrian navigation behavior.
method Reinforcement learning for collision avoidance and path planning.
result Path planning by the agent closely resembles human pedestrian behavior.
Paper proposes using expectation models for planning in stochastic environments.
problem Intractability of learning distribution and sample models in large state and action spaces.
method Proposes using approximate expectation models for MBRL, analyzes linear and non-linear parametrizations, and presents a policy evaluation algorithm.
result Planning with an expectation model is equivalent to planning with a distribution model under certain conditions.