SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Showing 56515700 of 15113 papers

TitleStatusHype
Is Pessimism Provably Efficient for Offline RL?0
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?0
Is Q-Learning Provably Efficient? An Extended Analysis0
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon0
Is RLHF More Difficult than Standard RL?0
Issues concerning realizability of Blackwell optimal policies in reinforcement learning0
Is the Bellman residual a bad proxy?0
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning0
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods0
Iteratively Learn Diverse Strategies with State Distance Information0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning0
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer0
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models0
Iterative Policy-Space Expansion in Reinforcement Learning0
Iterative Reachability Estimation for Safe Reinforcement Learning0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning0
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates0
J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge0
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization0
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications0
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning0
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading0
Job Scheduling in Datacenters using Constraint Controlled RL0
Data Centers Job Scheduling with Deep Reinforcement Learning0
Joint Attention for Multi-Agent Coordination and Social Learning0
Joint Band Assignment and Beam Management using Hierarchical Reinforcement Learning for Multi-Band Communication0
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment0
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning0
Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning0
Joint Entity Linking with Deep Reinforcement Learning0
Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation0
Joint Inference of Reward Machines and Policies for Reinforcement Learning0
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems0
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics0
Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture0
Jointly-Trained State-Action Embedding for Efficient Reinforcement Learning0
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment0
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments0
Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning0
Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm0
Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning0
Joint Representation Training in Sequential Tasks with Shared Structure0
Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach0
Joint Self-Supervised Learning for Vision-based Reinforcement Learning0
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G0
Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning0
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning0
JudgeLRM: Large Reasoning Models as a Judge0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76Unverified
2PPOMean Normalized Performance0.58Unverified