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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 351–375 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
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