Papers Unsupervised Reinforcement Learning
“Unsupervised Reinforcement Learning” 태그가 달린 논문 57편 · 필터 해제
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
Large and diverse datasets have been the cornerstones of many impressive advancements in artificial intelligence. Intelligent creatures, however, learn by interacting with the environment, which changes the input sensory…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Representation Learning+1Skill-Based Reinforcement Learning with Intrinsic Reward Matching
While unsupervised skill discovery has shown promise in autonomously acquiring behavioral primitives, there is still a large methodological disconnect between task-agnostic skill pretraining and downstream, task-aware fi…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Robot Manipulation+1A Mixture of Surprises for Unsupervised Reinforcement Learning
Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free manner for fast adaptation to downstream tasks. Most of the existing methods propose to provide an intrinsic reward based on surpr…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningEUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstr…
reinforcement-learningReinforcement Learning (RL)Unsupervised Pre-trainingUnsupervised Reinforcement LearningMastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but require large amounts of interactions between the agent and the environment. To alleviate …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningImpact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations
Sound is one of the most informative and abundant modalities in the real world while being robust to sense without contacts by small and cheap sensors that can be placed on mobile devices. Although deep learning is capab…
Efficient ExplorationUnsupervised Reinforcement LearningPOLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is improved. Although agents initialized wi…
Open-Ended Question Answeringreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only enable h…
GPUImitation LearningUnsupervised Reinforcement LearningUnsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery
Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm. To alleviate it, unsupervised RL, a framework that p…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningCIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors. CIC utilizes contrastive learning …
Contrastive LearningDiversityreinforcement-learningReinforcement Learning (RL)+1Unsupervised Reinforcement Learning in Multiple Environments
Several recent works have been dedicated to unsupervised reinforcement learning in a single environment, in which a policy is first pre-trained with unsupervised interactions, and then fine-tuned towards the optimal poli…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningURLB: Unsupervised Reinforcement Learning Benchmark
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to new tasks remains an outstanding challe…
continuous-controlContinuous ControlDeep Reinforcement Learningreinforcement-learning+3Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals and learn the goal-conditioned policy. How…
Domain Adaptationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Wasserstein Unsupervised Reinforcement Learning
Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward. These pre-trained policies can accelerate learning when endowed with external rew…
Hierarchical Reinforcement LearningMuJoCoreinforcement-learningReinforcement Learning+2The Information Geometry of Unsupervised Reinforcement Learning
How can a reinforcement learning (RL) agent prepare to solve downstream tasks if those tasks are not known a priori? One approach is unsupervised skill discovery, a class of algorithms that learn a set of policies withou…
Contrastive Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Rewardless Open-Ended Learning (ROEL)
Open-ended learning algorithms aim to automatically generate challenges and solutions to an unending sequence of learning opportunities. In Reinforcement Learning (RL) recent approaches to open-ended learning, such as Pa…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningUnsupervised multi-latent space reinforcement learning framework for video summarization in ultrasound imaging
The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. The proposed video-summarization technique is a step in th…
reinforcement-learningReinforcement Learning (RL)Unsupervised Reinforcement LearningVideo SummarizationAPS: Active Pretraining with Successor Features
We introduce a new unsupervised pretraining objective for reinforcement learning. During the unsupervised reward-free pretraining phase, the agent maximizes mutual information between tasks and states induced by the poli…
Unsupervised Reinforcement LearningMastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We…
continuous-controlContinuous ControlData AugmentationGPU+4Explore and Control with Adversarial Surprise
Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering. However, a central challenge in unsupervised RL is to extract beha…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement Learning