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Found 139 papers, 67 papers with code
Do Encoder Representations of Generative Dialogue Models have sufficient summary of the Information about the task ?
Predicting the next utterance in dialogue is contingent on encoding of users’ input text to generate appropriate and relevant response in data-driven approaches.
Sometimes We Want Ungrammatical Translations
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, and as a secondary focus, improving robustness to perturbations (e. g. spelling).
On the Societal Impact of Open Foundation Models
To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk.
The Curious Case of Absolute Position Embeddings
Transformer language models encode the notion of word order using positional information.
Questions Are All You Need to Train a Dense Passage Retriever
We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data.
Improving Passage Retrieval with Zero-Shot Question Generation
We propose a simple and effective re-ranking method for improving passage retrieval in open question answering.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
Estimating causal effects with optimization-based methods: A review and empirical comparison
In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given.
Robust Policy Learning over Multiple Uncertainty Sets
Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments.
A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions
We illustrate that incorporating predictive knowledge through an $\eta\gamma$-discounted SF model makes more efficient use of sampled experience, compared to either extreme, i. e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models.
Block Contextual MDPs for Continual Learning
In this work, we propose to examine this continual reinforcement learning setting through the block contextual MDP (BC-MDP) framework, which enables us to relax the assumption of stationarity.
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
We consider the offline reinforcement learning (RL) setting where the agent aims to optimize the policy solely from the data without further environment interactions.
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (NLL) or cross-entropy.
Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?
Predicting the next utterance in dialogue is contingent on encoding of users' input text to generate appropriate and relevant response in data-driven approaches.
SPeCiaL: Self-Supervised Pretraining for Continual Learning
This paper presents SPeCiaL: a method for unsupervised pretraining of representations tailored for continual learning.
Correcting Momentum in Temporal Difference Learning
A common optimization tool used in deep reinforcement learning is momentum, which consists in accumulating and discounting past gradients, reapplying them at each iteration.
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning (RL) setting.
Sometimes We Want Translationese
Rapid progress in Neural Machine Translation (NMT) systems over the last few years has been driven primarily towards improving translation quality, and as a secondary focus, improved robustness to input perturbations (e. g. spelling and grammatical mistakes).
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little
A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i. e., discovering protocols that can generalize to independently trained agents.
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration
To address this, we present a robust exploration strategy which enables causal hypothesis-testing by interaction with the environment.
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Unlike previous forms of state abstractions, a model-invariance state abstraction leverages causal sparsity over state variables.
Domain Adversarial Reinforcement Learning
We consider the problem of generalization in reinforcement learning where visual aspects of the observations might differ, e. g. when there are different backgrounds or change in contrast, brightness, etc.
Multi-Task Reinforcement Learning with Context-based Representations
We posit that an efficient approach to knowledge transfer is through the use of multiple context-dependent, composable representations shared across a family of tasks.
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods.
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0. 742 for predicting an adverse event within 96 hours (compared to 0. 703 with supervised pretraining) and an AUC of 0. 765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0. 749 with supervised pretraining).
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks
In this work, we study the logical generalization capabilities of GNNs by designing a benchmark suite grounded in first-order logic.
UnNatural Language Inference
We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i. e. they are largely invariant to random word-order permutations.
Intervention Design for Effective Sim2Real Transfer
The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting.
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels.
Regularized Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions.
Novelty Search in Representational Space for Sample Efficient Exploration
We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space for hard exploration tasks with sparse rewards.
Constrained Markov Decision Processes via Backward Value Functions
In standard RL, the agent is incentivized to explore any behavior as long as it maximizes rewards, but in the real world, undesired behavior can damage either the system or the agent in a way that breaks the learning process itself.
How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics
To bridge this gap in evaluation, we propose designing a set of probing tasks to evaluate dialogue models.
Learning Robust State Abstractions for Hidden-Parameter Block MDPs
Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work that use the same environment assumptions.
TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
We investigate whether Jacobi preconditioning, accounting for the bootstrap term in temporal difference (TD) learning, can help boost performance of adaptive optimizers.
Deep interpretability for GWAS
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator.
Plan2Vec: Unsupervised Representation Learning by Latent Plans
In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS).
Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
Learning an Unreferenced Metric for Online Dialogue Evaluation
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue.
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings.
Evaluating Logical Generalization in Graph Neural Networks
Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner.
Interference and Generalization in Temporal Difference Learning
We study the link between generalization and interference in temporal-difference (TD) learning.
Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.
Stable Policy Optimization via Off-Policy Divergence Regularization
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are among the most successful policy gradient approaches in deep reinforcement learning (RL).
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic
Multi-agent adversarial inverse reinforcement learning (MA-AIRL) is a recent approach that applies single-agent AIRL to multi-agent problems where we seek to recover both policies for our agents and reward functions that promote expert-like behavior.
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
We propose a general framework for policy representation for reinforcement learning tasks.
On the interaction between supervision and self-play in emergent communication
A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training.
Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research.
No-Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal.
Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking
The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process.
Online Learned Continual Compression with Adaptive Quantization Modules
We show how to use discrete auto-encoders to effectively address this challenge and introduce Adaptive Quantization Modules (AQM) to control variation in the compression ability of the module at any given stage of learning.
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift
This data distribution shift between current and past samples can significantly impact the performance of most modern off-policy based policy optimization algorithms.
Seeded self-play for language learning
Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language.
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
This is largely an artifact of building on top of frameworks designed for RL in games (e. g. OpenAI Gym).
Benchmarking Batch Deep Reinforcement Learning Algorithms
Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment.
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
A promising approach is to learn a latent representation together with the control policy.
Assessing Generalization in TD methods for Deep Reinforcement Learning
Current Deep Reinforcement Learning (DRL) methods can exhibit both data inefficiency and brittleness, which seem to indicate that they generalize poorly.
Online Learned Continual Compression with Stacked Quantization Modules
We first replace the episodic memory used in Experience Replay with SQM, leading to significant gains on standard continual learning benchmarks using a fixed memory budget.
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.
No Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal.
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.
Inductive logic programming
Natural Language Understanding
+2
Learning Causal State Representations of Partially Observable Environments
In this paper, we propose an algorithm to approximate causal states, which are the coarsest partition of the joint history of actions and observations in partially-observable Markov decision processes (POMDP).
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
We show that we can run several loosely coupled GALA agents in parallel on a single GPU and achieve significantly higher hardware utilization and frame-rates than vanilla A2C at comparable power draws.
Recurrent Value Functions
Despite recent successes in Reinforcement Learning, value-based methods often suffer from high variance hindering performance.
Leveraging exploration in off-policy algorithms via normalizing flows
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios.
On the Pitfalls of Measuring Emergent Communication
How do we know if communication is emerging in a multi-agent system?
Separating value functions across time-scales
In settings where this bias is unacceptable - where the system must optimize for longer horizons at higher discounts - the target of the value function approximator may increase in variance leading to difficulties in learning.
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
Deep Generative Modeling of LiDAR Data
In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.
Temporal Regularization for Markov Decision Process
Several applications of Reinforcement Learning suffer from instability due to high variance.
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning.
Natural Environment Benchmarks for Reinforcement Learning
While current benchmark reinforcement learning (RL) tasks have been useful to drive progress in the field, they are in many ways poor substitutes for learning with real-world data.
Compositional Language Understanding with Text-based Relational Reasoning
Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference.
The RLLChatbot: a solution to the ConvAI challenge
This article presents in detail the RLLChatbot that participated in the 2017 ConvAI challenge.
Language GANs Falling Short
Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks.
Adversarial Gain
Adversarial examples can be defined as inputs to a model which induce a mistake - where the model output is different than that of an oracle, perhaps in surprising or malicious ways.
Temporal Regularization in Markov Decision Process
Several applications of Reinforcement Learning suffer from instability due to high variance.
TarMAC: Targeted Multi-Agent Communication
We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments.
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods
We find that adaptive optimizers have a narrow window of effective learning rates, diverging in other cases, and that the effectiveness of momentum varies depending on the properties of the environment.
Extending Neural Generative Conversational Model using External Knowledge Sources
The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora.
Sequential Coordination of Deep Models for Learning Visual Arithmetic
Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive.
Combined Reinforcement Learning via Abstract Representations
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Deep reinforcement learning has recently shown many impressive successes.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
The risks and perils of overfitting in machine learning are well known.
Focused Hierarchical RNNs for Conditional Sequence Processing
We evaluate this method on several types of tasks with different attributes.
Ranked #3 on
Open-Domain Question Answering
on SearchQA
(Unigram Acc metric)
Randomized Value Functions via Multiplicative Normalizing Flows
In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function.
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
However, introduction of corrupt or stochastic rewards can yield high variance in learning.
Decoupling Dynamics and Reward for Transfer Learning
Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure.
Disentangling the independently controllable factors of variation by interacting with the world
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation.
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
An inference-based policy gradient method for learning options
In this work we develop a novel policy gradient method for the automatic learning of policies with options.
Multitask Spectral Learning of Weighted Automata
We first present a natural notion of relatedness between WFAs by considering to which extent several WFAs can share a common underlying representation.
Ethical Challenges in Data-Driven Dialogue Systems
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm.
ACtuAL: Actor-Critic Under Adversarial Learning
This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function.
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability.
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations.
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL).
A Deep Reinforcement Learning Chatbot
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem.
Independently Controllable Factors
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation.
Streaming kernel regression with provably adaptive mean, variance, and regularization
The variance of the noise is not assumed to be known.
Predicting Success in Goal-Driven Human-Human Dialogues
We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition.
MACA: A Modular Architecture for Conversational Agents
We propose a software architecture designed to ease the implementation of dialogue systems.
Independently Controllable Features
Finding features that disentangle the different causes of variation in real data is a difficult task, that has nonetheless received considerable attention in static domains like natural images.
Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus
In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
Piecewise Latent Variables for Neural Variational Text Processing
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders.
Generative Deep Neural Networks for Dialogue: A Short Review
Researchers have recently started investigating deep neural networks for dialogue applications.
Bayesian Reinforcement Learning: A Survey
The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.
Learning Robust Features using Deep Learning for Automatic Seizure Detection
We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures.
An Actor-Critic Algorithm for Sequence Prediction
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL).
Ranked #8 on
Machine Translation
on IWSLT2015 English-German
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue.
On the Evaluation of Dialogue Systems with Next Utterance Classification
An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data.
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available.
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models.
Conditional Computation in Neural Networks for faster models
In this paper, we use reinforcement learning as a tool to optimize conditional computation policies.
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models.
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
Practical Kernel-Based Reinforcement Learning
In this paper we introduce an algorithm that turns KBRL into a practical reinforcement learning tool.
Representation as a Service
Consider a Machine Learning Service Provider (MLSP) designed to rapidly create highly accurate learners for a never-ending stream of new tasks.
Non-Deterministic Policies in Markovian Decision Processes
Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medical domains, as they suggest policies that are often highly prescriptive and leave little room for the users input.
Online Planning Algorithms for POMDPs
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains.
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces
This paper addresses the problem of automatic generation of features for value function approximation in reinforcement learning.
Efficient Learning and Planning with Compressed Predictive States
Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems.
Learning from Limited Demonstrations
We achieve this by integrating LfD in an approximate policy iteration algorithm.
Online Ensemble Learning for Imbalanced Data Streams
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited.
End-to-End Text Recognition with Hybrid HMM Maxout Models
The problem of detecting and recognizing text in natural scenes has proved to be more challenging than its counterpart in documents, with most of the previous work focusing on a single part of the problem.
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization
The ability to learn a policy for a sequential decision problem with continuous state space using on-line data is a long-standing challenge.
PAC-Bayesian Policy Evaluation for Reinforcement Learning
PAC-Bayesian methods overcome this problem by providing bounds that hold regardless of the correctness of the prior distribution.
Reinforcement Learning using Kernel-Based Stochastic Factorization
Kernel-based reinforcement-learning (KBRL) is a method for learning a decision policy from a set of sample transitions which stands out for its strong theoretical guarantees.
PAC-Bayesian Model Selection for Reinforcement Learning
This paper introduces the first set of PAC-Bayesian bounds for the batch reinforcement learning problem in finite state spaces.
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
Interesting real-world datasets often exhibit nonlinear, noisy, continuous-valued states that are unexplorable, are poorly described by first principles, and are only partially observable.
Model-based Reinforcement Learning
reinforcement-learning
+1
MDPs with Non-Deterministic Policies
Markov Decision Processes (MDPs) have been extensively studied and used in the context of planning and decision-making, and many methods exist to find the optimal policy for problems modelled as MDPs.
Theoretical Analysis of Heuristic Search Methods for Online POMDPs
The algorithm uses search heuristics based on an error analysis of lookahead search, to guide the online search towards reachable beliefs with the most potential to reduce error.
