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 6126–6150 of 15113 papers

TitleStatusHype
Spatial Geometric Reasoning for Room Layout Estimation via Deep Reinforcement Learning—0
Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System—0
Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving—0
Spatial Positioning Token (SPToken) for Smart Parking—0
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning—0
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
Specification-Guided Learning of Nash Equilibria with High Social Welfare—0
Spectral Decomposition Representation for Reinforcement Learning—0
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning—0
Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion—0
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO—0
Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach—0
Speech Driven Backchannel Generation using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction—0
Speeding up reinforcement learning by combining attention and agency features—0
Spotlight: Optimizing Device Placement for Training Deep Neural Networks—0
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning—0
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin—0
SPP-RL: State Planning Policy Reinforcement Learning—0
Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning—0
Spreeze: High-Throughput Parallel Reinforcement Learning Framework—0
Square-root regret bounds for continuous-time episodic Markov decision processes—0
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model—0
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks—0
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning—0
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Benchmark Results

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