Search Results for author:
Found 36 papers, 11 papers with code
Tractable Offline Learning of Regular Decision Processes
In this paper, we show that it is possible to overcome two strong limitations of previous offline RL algorithms for RDPs, notably RegORL.
Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
We introduce a novel approach to hierarchical reinforcement learning for Linearly-solvable Markov Decision Processes (LMDPs) in the infinite-horizon average-reward setting.
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
We propose a new framework for formulating optimal transport distances between Markov chains.
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems.
Asymmetric Norms to Approximate the Minimum Action Distance
Our results show that our asymmetric norm parametrization performs comparably to symmetric norms in symmetric environments and surpasses symmetric norms in asymmetric environments.
Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects
First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i. e. the number of objects, state variables and their domain sizes).
Hierarchies of Reward Machines
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the task using high-level events.
Computing Programs for Generalized Planning as Heuristic Search
This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP.
Scaling-up Generalized Planning as Heuristic Search with Landmarks
In this paper we define a landmark counting heuristic for GP (that considers sub-goal information that is not explicitly represented in the planning instances), and a novel heuristic search algorithm for GP (that we call PGP) and that progressively processes subsets of the planning instances of a GP problem.
State Representation Learning for Goal-Conditioned Reinforcement Learning
This paper presents a novel state representation for reward-free Markov decision processes.
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
In this work we present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Hierarchical Representation Learning for Markov Decision Processes
In this paper we present a novel method for learning hierarchical representations of Markov decision processes.
Generalized Planning as Heuristic Search
First, the paper defines a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances.
Hierarchical Width-Based Planning and Learning
In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2.
Hierarchical reinforcement learning for efficient exploration and transfer
Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time.
Efficient Exploration
Hierarchical Reinforcement Learning
+2
Improved Exploration in Factored Average-Reward MDPs
We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP).
Induction and Exploitation of Subgoal Automata for Reinforcement Learning
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks.
Fast active learning for pure exploration in reinforcement learning
Realistic environments often provide agents with very limited feedback.
Hierarchical reinforcement learning for efficent exploration and transfer
Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Adaptive Reward-Free Exploration
Reward-free exploration is a reinforcement learning setting studied by Jin et al. (2020), who address it by running several algorithms with regret guarantees in parallel.
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for planning in a Markov Decision Process in which transitions have a finite support.
Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems.
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
We propose a novel model based reinforcement learning algorithm that is able to address both types of changes.
Model-based Reinforcement Learning
reinforcement-learning
+1
Induction of Subgoal Automata for Reinforcement Learning
In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL).
Generalized Planning with Positive and Negative Examples
Generalized planning aims at computing an algorithm-like structure (generalized plan) that solves a set of multiple planning instances.
Hierarchical Finite State Controllers for Generalized Planning
We show that hierarchical FSCs can represent generalized plans more compactly than individual FSCs.
Generalized Planning With Procedural Domain Control Knowledge
Generalized planning is the task of generating a single solution that is valid for a set of planning problems.
Solving Multiagent Planning Problems with Concurrent Conditional Effects
Empirically, we show that our compilation can solve challenging multiagent planning problems that require concurrent actions.
Deep Policies for Width-Based Planning in Pixel Domains
Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner.
Improving width-based planning with compact policies
The planning step hinges on the Iterated-Width (IW) planner, a state of the art planner that makes explicit use of the state representation to perform structured exploration.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
Implications of Decentralized Q-learning Resource Allocation in Wireless Networks
Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results.
A unified view of entropy-regularized Markov decision processes
We propose a general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes (MDPs).
Hierarchical Linearly-Solvable Markov Decision Problems
We present a hierarchical reinforcement learning framework that formulates each task in the hierarchy as a special type of Markov decision process for which the Bellman equation is linear and has analytical solution.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
The Role of Macros in Tractable Planning
We generalize the inverted tree reducible class in several ways and describe modifications of the algorithm to deal with these new classes.
Planning over Chain Causal Graphs for Variables with Domains of Size 5 Is NP-Hard
Recently, considerable focus has been given to the problem of determining the boundary between tractable and intractable planning problems.
