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

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Showing 3851–3900 of 15113 papers

TitleStatusHype
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models—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
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement LearningCode0
Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF—0
Breeding Programs Optimization with Reinforcement Learning—0
Bootstrapping Expectiles in Reinforcement Learning—0
Proofread: Fixes All Errors with One Tap—0
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms—0
Prompt-based Visual Alignment for Zero-shot Policy Transfer—0
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
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning—0
Reinforcement Learning with Lookahead Information—0
By Fair Means or Foul: Quantifying Collusion in a Market Simulation with Deep Reinforcement Learning—0
Rectifying Reinforcement Learning for Reward Matching—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
Learning the Target Network in Function Space—0
MOSEAC: Streamlined Variable Time Step Reinforcement LearningCode0
A Fast Convergence Theory for Offline Decision Making—0
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation—0
NeoRL: Efficient Exploration for Nonepisodic RL—0
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
REvolve: Reward Evolution with Large Language Models using Human Feedback—0
Causal prompting model-based offline reinforcement learning—0
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RLCode0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems—0
Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming—0
A Digital Twin Framework for Reinforcement Learning with Real-Time Self-Improvement via Human Assistive Teleoperation—0
Crafting a Pogo Stick in Minecraft with Heuristic Search (Extended Abstract)Code0
Bayesian Design Principles for Offline-to-Online Reinforcement LearningCode0
Reinforcement Learning for Sociohydrology—0
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
Hybrid Reinforcement Learning Framework for Mixed-Variable Problems—0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
Efficient Stimuli Generation using Reinforcement Learning in Design Verification—0
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity—0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF—0
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning—0
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Benchmark Results

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