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 4876–4900 of 15113 papers

TitleStatusHype
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes—0
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning—0
Machine-learning based noise characterization and correction on neutral atoms NISQ devices—0
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments—0
Estimating player completion rate in mobile puzzle games using reinforcement learning—0
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer—0
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
Decentralized Multi-Robot Formation Control Using Reinforcement Learning—0
Multivariate Time Series Early Classification Across Channel and Time DimensionsCode0
Supervised Pretraining Can Learn In-Context Reinforcement Learning—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
Is RLHF More Difficult than Standard RL?—0
A Framework for dynamically meeting performance objectives on a service mesh—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach—0
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams—0
Active Coverage for PAC Reinforcement Learning—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
MP3: Movement Primitive-Based (Re-)Planning Policy—0
Transferable Curricula through Difficulty Conditioned Generators—0
AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing OptimizationCode0
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory—0
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap—0
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Benchmark Results

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