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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 351–400 of 514 papers

TitleStatusHype
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Novelty Search in Representational Space for Sample Efficient ExplorationCode1
SEMI: Self-supervised Exploration via Multisensory Incongruity—0
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning—0
Occupancy Anticipation for Efficient Exploration and NavigationCode1
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement LearningCode1
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
See, Hear, Explore: Curiosity via Audio-Visual AssociationCode1
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic SystemsCode0
End-Effect Exploration Drive for Effective Motor Learning—0
Task-agnostic Exploration in Reinforcement Learning—0
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven ExplorationCode1
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
Multirobot Coverage of Modular EnvironmentsCode0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
Bayesian optimisation of large-scale photonic reservoir computers—0
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised LearningCode0
Active Model Estimation in Markov Decision Processes—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Scaling MAP-Elites to Deep NeuroevolutionCode1
Optimistic Exploration even with a Pessimistic InitialisationCode1
Efficient exploration of zero-sum stochastic games—0
Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy—0
Misspecification-robust likelihood-free inference in high dimensions—0
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization—0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement LearningCode0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Provably Efficient Exploration in Policy Optimization—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Better Exploration with Optimistic Actor CriticCode0
Comprehensive decision-strategy space exploration for efficient territorial planning strategies—0
Scaling active inference—0
Bayesian Curiosity for Efficient Exploration in Reinforcement LearningCode0
Implicit Generative Modeling for Efficient Exploration—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Neural Contextual Bandits with UCB-based ExplorationCode0
Multi-Path Policy Optimization—0
MAME : Model-Agnostic Meta-Exploration—0
Structured exploration in the finite horizon linear quadratic dual control problem—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
Better Exploration with Optimistic Actor-Critic—0
Learning Transferable Graph Exploration—0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
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