arXiv research

New approach handles stochastic and partially-observable environments using discrete autoencoders and Monte Carlo tree search.

Meta-learners estimate CATE from multiple environments with partial identification.

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic envi…

Policy gradient is a generic and flexible reinforcement learning approach that generally enjoys simplicity in analysis, implementation, and deployment. In the last few decades, this approach has been extensively advanced for fully observable environments. In this paper, we generalize a variety of these advances to part…

We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialog…

Recurrent networks learn beliefs from history in partially observable environments.

Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent's location within an environment or the presence of a predator, are often not directly observable but must be inferred using available sens…

The paper develops a method to learn navigation costs from expert demonstrations in partially observable environments.

Efficient RL in partially observable risk-sensitive environments with hindsight observations.

A method for self-supervised representation learning in partially observable environments.

DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.

A new RL algorithm tackles PO tasks by modeling the environment and improving the policy.

Paper tackles sim-to-real transfer in continuous domains with partial observations.

This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments. Off-policy evaluation under partial observability is inherently prone to bias, with risk of arbitrarily large errors. We define the problem of off-policy evaluation for Partially Observable Mark…

Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architec…

Algorithm improves imitation learning from visual data in partially observable environments.

A new metric detects non-Markovian states in partially observable environments.

AEC Games model represents software MARL environments better than POSGs.

In reinforcement learning algorithms, leveraging multiple views of the environment can improve the learning of complicated policies. In multi-view environments, due to the fact that the views may frequently suffer from partial observability, their level of importance are often different. In this paper, we propose a dee…

Enhances RL in partially observable, noisy environments by uncovering causal states.

When faced with the problem of learning a model of a high-dimensional environment, a common approach is to limit the model to make only a restricted set of predictions, thereby simplifying the learning problem. These partial models may be directly useful for making decisions or may be combined together to form a more c…

The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep…

Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments with partial or noisy observations. In partially observable domains it is important for the representation to encode a belief state, a sufficie…

Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. 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). Our m…

We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its …

Stable Hadamard Memory improves reinforcement learning by efficiently managing memory.

HRM-Agent learns to navigate dynamic mazes using reinforcement learning.

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation mod…

Many interesting real world domains involve reinforcement learning (RL) in partially observable environments. Efficient learning in such domains is important, but existing sample complexity bounds for partially observable RL are at least exponential in the episode length. We give, to our knowledge, the first partially …

NBF combines deep learning with classical filtering for better belief tracking.

The paper explores how agents can generalize to new environments with unseen variables.

In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies. Since the views may frequently suffer from partial observabilit…

AdaptOn achieves logarithmic regret in adaptive control of unknown partially observable linear systems.

Enhances reinforcement learning with partial state information.

UWM-JEPA predicts future scenarios in belief space, improving accuracy in partially observed environments.

Study optimal policy regret in partially observable Markov games with adaptive opponents.

This work uses reinforcement learning to optimize task scheduling and execution in a dynamic multi-agent warehouse environment.

RLHF fails when humans only partially observe, leading to inflated or overjustified feedback.

A novel memory mechanism for reinforcement learning agents that stores past events in human-readable language.

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-s…

FEPS models agents to learn and act in complex environments without deep neural networks.

Smart grid uses deep learning to optimize household energy use.

Humans achieve efficient learning by relying on prior knowledge about the structure of naturally occurring tasks. There is considerable interest in designing reinforcement learning (RL) algorithms with similar properties. This includes proposals to learn the learning algorithm itself, an idea also known as meta learnin…

Paper introduces MVS to detect non-Markovian observations in reinforcement learning.

Improved ExO method achieves near-optimal bounds in both stochastic and adversarial settings.

Study optimizes inspection and monitoring of deteriorating structures using POMDPs.

DPFRL uses particle filters for decision making with complex visual observations.

A new algorithm trains experts to safely guide agents in partially observed environments.