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Found 18 papers, 12 papers with code
Embodied Active Learning of Relational State Abstractions for Bilevel Planning
State abstraction is an effective technique for planning in robotics environments with continuous states and actions, long task horizons, and sparse feedback.
Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning
Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision-making, is an effective approach to solving long-horizon tasks in continuous state and action spaces.
Learning Neuro-Symbolic Skills for Bilevel Planning
Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback.
PG3: Policy-Guided Planning for Generalized Policy Generation
In this work, we study generalized policy search-based methods with a focus on the score function used to guide the search over policies.
Predicate Invention for Bilevel Planning
Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective.
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators
In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL.
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons.
Learning Symbolic Operators for Task and Motion Planning
We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.
Online Bayesian Goal Inference for Boundedly Rational Planning Agents
These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.
Integrated Task and Motion Planning
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP).
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances.
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.
PDDLGym: Gym Environments from PDDL Problems
We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems.
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards.
Few-Shot Bayesian Imitation Learning with Logical Program Policies
We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples.
Residual Policy Learning
In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvements.
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks.
