Papers Unsupervised Reinforcement Learning
“Unsupervised Reinforcement Learning” 태그가 달린 논문 57편 · 필터 해제
Behavior From the Void: Unsupervised Active Pre-Training
We introduce a new unsupervised pre-training method for reinforcement learning called APT, which stands for Active Pre-Training. APT learns behaviors and representations by actively searching for novel states in reward-f…
Atari GamesReinforcement Learning (RL)Unsupervised Pre-trainingUnsupervised Reinforcement LearningReinforcement Learning with Prototypical Representations
Learning effective representations in image-based environments is crucial for sample efficient Reinforcement Learning (RL). Unfortunately, in RL, representation learning is confounded with the exploratory experience of t…
continuous-controlContinuous Controlreinforcement-learningReinforcement Learning+3Variational Intrinsic Control Revisited
In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work by Gregor et al. (…
reinforcement-learningReinforcement Learning (RL)Unsupervised Reinforcement LearningEmergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort. However, most reinforcement learning algorithms assume that a well-designed reward function is provided…
Model Predictive Controlreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence …
Navigatereinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Analysis of Evolutionary Behavior in Self-Learning Media Search Engines
The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a SelfLearning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught a…
Diversityreinforcement-learningReinforcement LearningReinforcement Learning (RL)+3Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e.g., television) using only visual observations. A key challenge for current deep reinforcement learning models l…
Deep Reinforcement LearningObjectreinforcement-learningReinforcement Learning+3SMiRL: Surprise Minimizing RL in Entropic Environments
All living organisms struggle against the forces of nature to carve out niches where they can maintain relative stasis. We propose that such a search for order amidst chaos might offer a unifying principle for the emerge…
Unsupervised Pre-trainingUnsupervised Reinforcement LearningMachine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks
The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing at…
BIG-bench Machine LearningReinforcement LearningUnsupervised Reinforcement LearningEfficient Exploration via State Marginal Matching
Exploration is critical to a reinforcement learning agent's performance in its given environment. Prior exploration methods are often based on using heuristic auxiliary predictions to guide policy behavior, lacking a mat…
Efficient ExplorationReinforcement LearningUnsupervised Reinforcement LearningSelf-Supervised Exploration via Disagreement
Efficient exploration is a long-standing problem in sensorimotor learning. Major advances have been demonstrated in noise-free, non-stochastic domains such as video games and simulation. However, most of these formulatio…
Active LearningEfficient ExplorationMuJoCoReinforcement Learning+2Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
Exploration in environments with sparse rewards is a key challenge for reinforcement learning. How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using l…
Atari GamesDeep Reinforcement LearningHierarchical Reinforcement Learningreinforcement-learning+3CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments
In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, wit…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningExploration by Random Network Distillation
We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features o…
Atari GamesDeep Reinforcement LearningMontezuma's Revengereinforcement-learning+3Diversity is All You Need: Learning Skills without a Reward Function
Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose DIAYN ('Diversity is All You Need'), a method for learning useful skills without a reward functi…
AllDiversityMeta Reinforcement Learningreinforcement-learning+3Curiosity-driven Exploration by Self-supervised Prediction
In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and…
PredictionUnsupervised Reinforcement LearningVariational Intrinsic Control
In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent ca…
Reinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement Learning