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 3851–3875 of 15113 papers

TitleStatusHype
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement LearningCode0
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models—0
Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF—0
Proofread: Fixes All Errors with One Tap—0
Breeding Programs Optimization with Reinforcement Learning—0
Bootstrapping Expectiles in Reinforcement Learning—0
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement LearningCode0
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation—0
UDQL: Bridging The Gap between MSE Loss and The Optimal Value Function in Offline Reinforcement Learning—0
Prompt-based Visual Alignment for Zero-shot Policy Transfer—0
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
Rectifying Reinforcement Learning for Reward Matching—0
Reinforcement Learning with Lookahead Information—0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning—0
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling—0
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning—0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
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Benchmark Results

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