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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

Showing 401450 of 514 papers

TitleStatusHype
Better Exploration with Optimistic Actor-Critic0
Learning Transferable Graph Exploration0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
Receding Horizon CuriosityCode0
Deep exploration by novelty-pursuit with maximum state entropy0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
Regulatory Focus: Promotion and Prevention Inclinations in Policy Search0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration0
Learning Index Selection with Structured Action Spaces0
Biased Estimates of Advantages over Path Ensembles0
n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank0
Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining0
Learning to Explore in Motion and Interaction Tasks0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards0
Directed Exploration for Reinforcement Learning0
Learning-Driven Exploration for Reinforcement LearningCode0
Efficient Exploration via State Marginal MatchingCode0
Learning to Score Behaviors for Guided Policy OptimizationCode0
Worst-Case Regret Bounds for Exploration via Randomized Value Functions0
Clustered Reinforcement Learning0
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning0
Distributional Reinforcement Learning for Efficient Exploration0
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework0
Beyond Games: Bringing Exploration to Robots in Real-world0
Explicit Recall for Efficient Exploration0
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human PriorsCode0
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context VariablesCode0
Concurrent Meta Reinforcement LearningCode0
Bayesian Reinforcement Learning via Deep, Sparse SamplingCode0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
Context-Dependent Upper-Confidence Bounds for Directed Exploration0
Incentivizing Exploration with Selective Data Disclosure0
DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network0
Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates0
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningCode0
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic AlgorithmCode0
Thompson Sampling Algorithms for Cascading Bandits0
Exploration by Uncertainty in Reward Space0
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Directed Exploration in PAC Model-Free Reinforcement Learning0
Discovering Context Specific Causal Relationships0
Count-Based Exploration with the Successor RepresentationCode0
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