{"total":95,"rows":[{"id":"2205.00824","title":"Exploration in Deep Reinforcement Learning: A Survey","created":"2022-05-02","cited_by_count":166,"area":null},{"id":"2102.09430","title":"State Entropy Maximization with Random Encoders for Efficient Exploration","created":"2021-02-18","cited_by_count":108,"area":null},{"id":"2109.00157","title":"A Survey of Exploration Methods in Reinforcement Learning","created":"2021-09-01","cited_by_count":73,"area":null},{"id":"2109.06668","title":"Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain","created":"2021-09-14","cited_by_count":58,"area":null},{"id":"2310.19668","title":"DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization","created":"2023-10-30","cited_by_count":47,"area":null},{"id":"2210.11653","title":"PaCo: Parameter-Compositional Multi-Task Reinforcement Learning","created":"2022-10-21","cited_by_count":46,"area":null},{"id":"2210.01620","title":"SAM as an Optimal Relaxation of Bayes","created":"2022-10-04","cited_by_count":41,"area":null},{"id":"2106.10268","title":"MADE: Exploration via Maximizing Deviation from Explored Regions","created":"2021-06-18","cited_by_count":40,"area":null},{"id":"2310.03882","title":"Small batch deep reinforcement learning","created":"2023-10-05","cited_by_count":36,"area":null},{"id":"2509.24372","title":"Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning","created":"2025-09-29","cited_by_count":34,"area":null},{"id":"2303.04150","title":"Evolutionary Reinforcement Learning: A Survey","created":"2023-03-07","cited_by_count":29,"area":null},{"id":"2305.18246","title":"Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo","created":"2023-05-29","cited_by_count":29,"area":null},{"id":"2504.15266","title":"Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction","created":"2025-04-21","cited_by_count":29,"area":null},{"id":"2209.08890","title":"An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey","created":"2022-09-19","cited_by_count":28,"area":null},{"id":"2110.15335","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","created":"2021-10-28","cited_by_count":24,"area":null},{"id":"2408.05804","title":"A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals","created":"2024-08-11","cited_by_count":23,"area":null},{"id":"2206.03787","title":"Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance","created":"2022-06-08","cited_by_count":22,"area":null},{"id":"2305.19476","title":"Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration","created":"2023-05-31","cited_by_count":22,"area":null},{"id":"2305.20065","title":"Latent Exploration for Reinforcement Learning","created":"2023-05-31","cited_by_count":22,"area":null},{"id":"2510.11696","title":"QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs","created":"2025-10-13","cited_by_count":16,"area":null},{"id":"2102.05710","title":"Derivative-Free Reinforcement Learning: A Review","created":"2021-02-10","cited_by_count":15,"area":null},{"id":"2101.09458","title":"Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning","created":"2021-01-23","cited_by_count":14,"area":null},{"id":"2205.10047","title":"The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure","created":"2022-05-20","cited_by_count":12,"area":null},{"id":"2305.18443","title":"Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse","created":"2023-05-29","cited_by_count":12,"area":null},{"id":"2110.04422","title":"Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation","created":"2021-10-09","cited_by_count":11,"area":null},{"id":"2108.04526","title":"A Survey on Deep Reinforcement Learning for Data Processing and Analytics","created":"2021-08-10","cited_by_count":10,"area":null},{"id":"2201.09765","title":"Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning","created":"2022-01-24","cited_by_count":10,"area":null},{"id":"2302.09339","title":"Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization","created":"2023-02-18","cited_by_count":10,"area":null},{"id":"2203.00715","title":"Learning Robust Real-Time Cultural Transmission without Human Data","created":"2022-03-01","cited_by_count":9,"area":null},{"id":"2309.15293","title":"Maximum diffusion reinforcement learning","created":"2023-09-26","cited_by_count":9,"area":null},{"id":"2111.10898","title":"Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning","created":"2021-11-21","cited_by_count":8,"area":null},{"id":"2111.15382","title":"Continuous Control With Ensemble Deep Deterministic Policy Gradients","created":"2021-11-30","cited_by_count":8,"area":null},{"id":"2312.11091","title":"Colored Noise in PPO: Improved Exploration and Performance through Correlated Action Sampling","created":"2023-12-18","cited_by_count":8,"area":null},{"id":"2105.09992","title":"Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness","created":"2021-05-20","cited_by_count":7,"area":null},{"id":"2301.02083","title":"Self-Motivated Multi-Agent Exploration","created":"2023-01-05","cited_by_count":7,"area":null},{"id":"2102.03719","title":"State-Aware Variational Thompson Sampling for Deep Q-Networks","created":"2021-02-07","cited_by_count":5,"area":null},{"id":"2112.11115","title":"Soft Actor-Critic with Cross-Entropy Policy Optimization","created":"2021-12-21","cited_by_count":5,"area":null},{"id":"2305.02949","title":"Rethinking Population-assisted Off-policy Reinforcement Learning","created":"2023-05-04","cited_by_count":5,"area":null},{"id":"2210.00293","title":"Deep Intrinsically Motivated Exploration in Continuous Control","created":"2022-10-01","cited_by_count":4,"area":null},{"id":"2212.14374","title":"Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks","created":"2022-12-29","cited_by_count":4,"area":null},{"id":"2303.04012","title":"Exploration via Epistemic Value Estimation","created":"2023-03-07","cited_by_count":4,"area":null},{"id":"2303.14265","title":"Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2306.17052","title":"Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning","created":"2023-06-29","cited_by_count":4,"area":null},{"id":"2308.14111","title":"MARL for Decentralized Electric Vehicle Charging Coordination with V2V Energy Exchange","created":"2023-08-27","cited_by_count":4,"area":null},{"id":"2203.01004","title":"Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise","created":"2022-03-02","cited_by_count":3,"area":null},{"id":"2509.10656","title":"Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration","created":"2025-09-12","cited_by_count":3,"area":null},{"id":"2604.15614","title":"Flexible Empowerment at Reasoning with Extended Best-of-N Sampling","created":"2026-04-17","cited_by_count":3,"area":null},{"id":"2105.04683","title":"Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks","created":"2021-05-10","cited_by_count":2,"area":null},{"id":"2107.08293","title":"On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems","created":"2021-07-17","cited_by_count":2,"area":null},{"id":"2107.11500","title":"$\\mu$DARTS: Model Uncertainty-Aware Differentiable Architecture Search","created":"2021-07-24","cited_by_count":2,"area":null},{"id":"2202.09052","title":"Tackling benign nonconvexity with smoothing and stochastic gradients","created":"2022-02-18","cited_by_count":2,"area":null},{"id":"2203.02298","title":"Intrinsically-Motivated Reinforcement Learning: A Brief Introduction","created":"2022-03-03","cited_by_count":2,"area":null},{"id":"2206.05652","title":"Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies","created":"2022-06-12","cited_by_count":2,"area":null},{"id":"2206.09627","title":"Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration","created":"2022-06-20","cited_by_count":2,"area":null},{"id":"2302.04693","title":"Scaling Goal-based Exploration via Pruning Proto-goals","created":"2023-02-09","cited_by_count":2,"area":null},{"id":"2304.06056","title":"Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation","created":"2023-04-12","cited_by_count":2,"area":null},{"id":"2309.12560","title":"Machine Learning Meets Advanced Robotic Manipulation","created":"2023-09-22","cited_by_count":2,"area":null},{"id":"2312.16456","title":"Adaptive trajectory-constrained exploration strategy for deep reinforcement learning","created":"2023-12-27","cited_by_count":2,"area":null},{"id":"2607.16183","title":"A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing","created":"2026-07-17","cited_by_count":2,"area":null},{"id":"2110.10934","title":"Can Q-learning solve Multi Armed Bantids?","created":"2021-10-21","cited_by_count":1,"area":null},{"id":"2201.12518","title":"Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems","created":"2022-01-29","cited_by_count":1,"area":null},{"id":"2202.11514","title":"A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles","created":"2022-02-22","cited_by_count":1,"area":null},{"id":"2205.07592","title":"Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents","created":"2022-05-16","cited_by_count":1,"area":null},{"id":"2205.09705","title":"Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2209.14488","title":"Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training","created":"2022-09-29","cited_by_count":1,"area":null},{"id":"2211.15205","title":"CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control","created":"2022-11-28","cited_by_count":1,"area":null},{"id":"2310.11075","title":"Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance","created":"2023-10-17","cited_by_count":1,"area":null},{"id":"2311.12975","title":"Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2504.21023","title":"Param$\\Delta$ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost","created":"2025-04-23","cited_by_count":1,"area":null},{"id":"2512.25063","title":"Many Minds from One Model: Bayesian-Inspired Transformers for Population Diversity","created":"2025-12-31","cited_by_count":1,"area":null},{"id":"2608.09805","title":"Parameter Exploration for RLVR via Variational Learning","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2101.00355","title":"Reinforcement Learning for Flexibility Design Problems","created":"2021-01-02","cited_by_count":0,"area":null},{"id":"2104.04078","title":"Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks","created":"2021-04-06","cited_by_count":0,"area":null},{"id":"2110.12081","title":"Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction","created":"2021-10-22","cited_by_count":0,"area":null},{"id":"2203.10494","title":"MicroRacer: a didactic environment for Deep Reinforcement Learning","created":"2022-03-20","cited_by_count":0,"area":null},{"id":"2205.02944","title":"A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior","created":"2022-05-05","cited_by_count":0,"area":null},{"id":"2208.14689","title":"Deep Reinforcement Learning for Uplink Multi-Carrier Non-Orthogonal Multiple Access Resource Allocation Using Buffer State Information","created":"2022-08-31","cited_by_count":0,"area":null},{"id":"2211.17013","title":"Climate Change Policy Exploration using Reinforcement Learning","created":"2022-10-23","cited_by_count":0,"area":null},{"id":"2212.10712","title":"Neighboring State-based Exploration for Reinforcement Learning","created":"2022-12-21","cited_by_count":0,"area":null},{"id":"2302.13268","title":"Revolutionizing Genomics with Reinforcement Learning Techniques","created":"2023-02-26","cited_by_count":0,"area":null},{"id":"2304.13223","title":"Reinforcement Learning with Partial Parametric Model Knowledge","created":"2023-04-26","cited_by_count":0,"area":null},{"id":"2310.01517","title":"The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2310.17173","title":"DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic","created":"2023-10-26","cited_by_count":0,"area":null},{"id":"2312.03008","title":"Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2407.08065","title":"Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach","created":"2024-07-10","cited_by_count":0,"area":null},{"id":"2506.01568","title":"Trajectory First: A Curriculum for Discovering Diverse Policies","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2510.12447","title":"Bayesian Optimization for Dynamic Pricing and Learning","created":"2025-10-14","cited_by_count":0,"area":null},{"id":"2601.21182","title":"Rethinking Refinement: Correcting Generative Bias without Noise Injection","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2606.12054","title":"Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2607.08837","title":"Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2607.17965","title":"Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2608.13800","title":"Stochastic Control Policies for Robust Molecular Transition Path Sampling","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.23258","title":"Progressively Learning Heterogeneous Skills in a Unified Latent Space","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.23269","title":"Latent Telepathy: Multi-Robot Communication with Self-Supervised Perceptual Latents","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.33589","title":"TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}