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 1951–1975 of 15113 papers

TitleStatusHype
Bootstrapping Expectiles in Reinforcement Learning—0
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement LearningCode1
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models—0
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement LearningCode0
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
UDQL: Bridging The Gap between MSE Loss and The Optimal Value Function in Offline Reinforcement Learning—0
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation—0
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms—0
Prompt-based Visual Alignment for Zero-shot Policy Transfer—0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning—0
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement LearningCode1
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning—0
Reinforcement Learning with Lookahead Information—0
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling—0
Rectifying Reinforcement Learning for Reward Matching—0
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
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Benchmark Results

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