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Found 36 papers, 10 papers with code
Variational Inference for Sequential Data with Future Likelihood Estimates
The estimated future likelihoods form the core of our new low-variance gradient estimator.
Batch Reinforcement Learning with Hyperparameter Gradients
We consider the batch reinforcement learning problem where the agent needs to learn only from a fixed batch of data, without further interaction with the environment.
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents
The Situated Interactive Multi-Modal Conversations (SIMMC) 2. 0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i. e. visual appearances of objects and user utterances.
Ranked #2 on
Response Generation
on SIMMC2.0
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
We argue that when we extract knowledge from source tasks via training source prompts, we need to consider this correlation among source tasks for better transfer to target tasks.
Stitching Sub-Trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL
In this paper, we propose SSD (Sub-trajectory Stitching with Diffusion), a model-based offline GCRL method that leverages the conditional diffusion model to address these limitations.
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
To this end, we introduce AlberDICE, an offline MARL algorithm that alternatively performs centralized training of individual agents based on stationary distribution optimization.
Adapting Text-based Dialogue State Tracker for Spoken Dialogues
Although there have been remarkable advances in dialogue systems through the dialogue systems technology competition (DSTC), it remains one of the key challenges to building a robust task-oriented dialogue system with a speech interface.
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces.
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning
Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task.
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning only from a pre-collected dataset.
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
We consider the problem of learning from observation (LfO), in which the agent aims to mimic the expert's behavior from the state-only demonstrations by experts.
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
For this reason, data-centric approaches are crucial for the automation of machine learning operation pipeline.
Multi-View Representation Learning via Total Correlation Objective
Multi-View Representation Learning (MVRL) aims to discover a shared representation of observations from different views with the complex underlying correlation.
Offline Reinforcement Learning for Large Scale Language Action Spaces
GPT-Critic is essentially free from the issue of diverging from human language since it learns from the sentences sampled from the pre-trained language model.
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning
We empirically show that the morphological information is crucial for modular reinforcement learning, substantially outperforming prior state-of-the-art methods on multi-task learning as well as transfer learning settings with different state and action space dimensions.
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
We consider offline imitation learning (IL), which aims to mimic the expert's behavior from its demonstration without further interaction with the environment.
Dual Correction Strategy for Ranking Distillation in Top-N Recommender System
Knowledge Distillation (KD), which transfers the knowledge of a well-trained large model (teacher) to a small model (student), has become an important area of research for practical deployment of recommender systems.
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.
Monte-Carlo Planning and Learning with Language Action Value Estimates
Interactive Fiction (IF) games provide a useful testbed for language-based reinforcement learning agents, posing significant challenges of natural language understanding, commonsense reasoning, and non-myopic planning in the combinatorial search space.
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
Safe and reliable electricity transmission in power grids is crucial for modern society.
Representation Balancing Offline Model-based Reinforcement Learning
We present a new objective for model learning motivated by recent advances in the estimation of stationary distribution corrections.
Variational Interaction Information Maximization for Cross-domain Disentanglement
Grounded in information theory, we cast the simultaneous learning of domain-invariant and domain-specific representations as a joint objective of multiple information constraints, which does not require adversarial training or gradient reversal layers.
Reinforcement Learning for Control with Multiple Frequencies
Many real-world sequential decision problems involve multiple action variables whose control frequencies are different, such that actions take their effects at different periods.
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2
The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal.
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems.
Policy Optimization Through Approximate Importance Sampling
These proxy objectives allow stable and low variance policy learning, but require small policy updates to ensure that the proxy objective remains an accurate approximation of the target policy value.
Monte-Carlo Tree Search for Constrained POMDPs
In this paper, we present CC-POMCP (Cost-Constrained POMCP), an online MCTS algorithm for large CPOMDPs that leverages the optimization of LP-induced parameters and only requires a black-box simulator of the environment.
A Bayesian Approach to Generative Adversarial Imitation Learning
Generative adversarial training for imitation learning has shown promising results on high-dimensional and continuous control tasks.
Generative Local Metric Learning for Kernel Regression
This paper shows how metric learning can be used with Nadaraya-Watson (NW) kernel regression.
Information-Theoretic Bounded Rationality
Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics.
Learning to Cooperate via Policy Search
Cooperative games are those in which both agents share the same payoff structure.
Cost-Sensitive Exploration in Bayesian Reinforcement Learning
In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected long-term total reward.
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions
We present a nonparametric Bayesian approach to inverse reinforcement learning (IRL) for multiple reward functions.
MAP Inference for Bayesian Inverse Reinforcement Learning
The difficulty in inverse reinforcement learning (IRL) arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behaviour data as optimal.
