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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5651–5675 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 Analysis—0
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Is RLHF More Difficult than Standard RL?—0
Issues concerning realizability of Blackwell optimal policies in reinforcement learning—0
Is the Bellman residual a bad proxy?—0
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning—0
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods—0
Iteratively Learn Diverse Strategies with State Distance Information—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer—0
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models—0
Iterative Policy-Space Expansion in Reinforcement Learning—0
Iterative Reachability Estimation for Safe Reinforcement Learning—0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning—0
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates—0
J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge—0
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization—0
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application—0
"Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications—0
Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning—0
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading—0
Job Scheduling in Datacenters using Constraint Controlled RL—0
Data Centers Job Scheduling with Deep Reinforcement Learning—0
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Benchmark Results

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