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Efficient Exploration in Reinforcement Learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Empirical comparison of various reinforcement learning strategies for sequential targeted marketing
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Reinforcement Learning: An Introduction
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PEGASUS: A Policy Search Method for Large MDPs and POMDPs
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Model-Based Bayesian Exploration
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On the Complexity of Solving Markov Decision Problems
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The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
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Solving Battle Management/Command Control and Communication Problem using Modified BIONET
inferred← citeshttps://doi.org/10.14429/dsj.57.1744openalex-snapshot
Missile Defence and Interceptor Allocation by LVQ-RBFMulti-agent Hybrid Architecture
inferred← citeshttps://doi.org/10.14569/ijacsa.2013.041025openalex-snapshot
Terrain Coverage Ant Algorithms: The Random Kick Effect
inferred← citeshttps://doi.org/10.15368/theses.2018.170openalex-snapshot
Influencing Exploration in Actor-Critic Reinforcement Learning Algorithms
inferred← citeshttps://doi.org/10.15388/informatica.2003.018openalex-snapshot
Efficient Exploration in Reinforcement Learning Based on Utile Suffix Memory
inferred← citeshttps://doi.org/10.1561/2200000086openalex-snapshot
Model-based Reinforcement Learning: A Survey
inferred← citeshttps://doi.org/10.1609/aaai.v26i1.8286openalex-snapshot
TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration
inferred← citeshttps://doi.org/10.1609/aaai.v35i12.17316openalex-snapshot
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
inferred← citeshttps://doi.org/10.1609/aaai.v35i7.16765openalex-snapshot
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
inferred← citeshttps://doi.org/10.1680/jtran.17.00085openalex-snapshot
Developing adaptive traffic signal control by actor–critic and direct exploration methods
inferred← citeshttps://doi.org/10.17863/cam.16688openalex-snapshot
Bayesian Learning for Data-Efficient Control
inferred← citeshttps://doi.org/10.23919/acc.2018.8431396openalex-snapshot
Designing Input Shapers Using Reinforcement Learning
inferred← citeshttps://doi.org/10.2478/s13230-013-0108-6openalex-snapshot
Adaptive Exploration through Covariance Matrix Adaptation Enables Developmental Motor Learning
inferred← citeshttps://doi.org/10.3233/kes-2008-125-606openalex-snapshot
Effects of chaotic exploration on reinforcement learning in target capturing task
inferred← citeshttps://doi.org/10.3389/fnbot.2018.00032openalex-snapshot
Experience Replay Using Transition Sequences
inferred← citeshttps://doi.org/10.3389/fncir.2013.00037openalex-snapshot
Learning and exploration in action-perception loops
inferred← citeshttps://doi.org/10.3390/g6030251openalex-snapshot
Unfazed by Both the Bull and Bear: Strategic Exploration in Dynamic Environments
inferred← citeshttps://doi.org/10.48550/arxiv.0903.4930openalex-snapshot
Time manipulation technique for speeding up reinforcement learning in simulations
inferred← citeshttps://doi.org/10.48550/arxiv.1302.4966openalex-snapshot
Probabilistic Exploration in Planning while Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1809.05188openalex-snapshot
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1902.10646openalex-snapshot
Unifying Ensemble Methods for Q-learning via Social Choice Theory
inferred← citeshttps://doi.org/10.48550/arxiv.1910.03732openalex-snapshot
Ctrl-Z: Recovering from Instability in Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1910.04721openalex-snapshot
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification
inferred← citeshttps://doi.org/10.48550/arxiv.2004.10876openalex-snapshot
Flexible and Efficient Long-Range Planning Through Curious Exploration
inferred← citeshttps://doi.org/10.48550/arxiv.2007.03433openalex-snapshot
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control
inferred← citeshttps://doi.org/10.48550/arxiv.2009.05524openalex-snapshot
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2009.09842openalex-snapshot
Energy-based Surprise Minimization for Multi-Agent Value Factorization
inferred← citeshttps://doi.org/10.48550/arxiv.2009.13579openalex-snapshot
Novelty Search in Representational Space for Sample Efficient Exploration
inferred← citeshttps://doi.org/10.48550/arxiv.2009.14297openalex-snapshot
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network
inferred← citeshttps://doi.org/10.48550/arxiv.2010.15533openalex-snapshot
How do Offline Measures for Exploration in Reinforcement Learning behave?
inferred← citeshttps://doi.org/10.48550/arxiv.2011.04950openalex-snapshot
Model-based Reinforcement Learning from Signal Temporal Logic Specifications
inferred← citeshttps://doi.org/10.48550/arxiv.2103.01948openalex-snapshot
Offline Reinforcement Learning with Pseudometric Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2105.08990openalex-snapshot
Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2105.09719openalex-snapshot
Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics
inferred← citeshttps://doi.org/10.48550/arxiv.2105.09992openalex-snapshot
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
inferred← citeshttps://doi.org/10.48550/arxiv.2106.05526openalex-snapshot
Simplifying Deep Reinforcement Learning via Self-Supervision
inferred← citeshttps://doi.org/10.48550/arxiv.2108.11811openalex-snapshot
When should agents explore?
inferred← citeshttps://doi.org/10.48550/arxiv.2109.00157openalex-snapshot
A Survey of Exploration Methods in Reinforcement Learning
inferred← citeshttps://doi.org/10.51257/a-v1-s7809openalex-snapshot
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