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 11751–11800 of 15113 papers

TitleStatusHype
Reward Gaming in Conditional Text Generation—0
Task Aware Dreamer for Task Generalization in Reinforcement Learning—0
Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning—0
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation—0
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning—0
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue—0
Reward is enough for convex MDPs—0
Reward Is Enough: LLMs Are In-Context Reinforcement Learners—0
Reward is not enough: can we liberate AI from the reinforcement learning paradigm?—0
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery—0
Reward Learning using Structural Motifs in Inverse Reinforcement Learning—0
Rewardless Open-Ended Learning (ROEL)—0
Reward Machine Inference for Robotic Manipulation—0
Reward (Mis)design for Autonomous Driving—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments—0
Reward prediction for representation learning and reward shaping—0
Reward-Predictive Clustering—0
STIR^2: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks—0
Reward-Respecting Subtasks for Model-Based Reinforcement Learning—0
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning—0
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives—0
Reward Shaping for User Satisfaction in a REINFORCE Recommender—0
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Reward Shaping via Meta-Learning—0
Reward Shaping with Dynamic Trajectory Aggregation—0
Reward Shaping with Subgoals for Social Navigation—0
RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation—0
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization—0
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective—0
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning—0
REX: Rapid Exploration and eXploitation for AI Agents—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration—0
Riemannian Stochastic Gradient Method for Nested Composition Optimization—0
RILe: Reinforced Imitation Learning—0
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning—0
RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources—0
Risk-Averse Bayes-Adaptive Reinforcement Learning—0
Risk-Averse Learning by Temporal Difference Methods—0
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Risk Averse Robust Adversarial Reinforcement Learning—0
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning—0
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search—0
Risk-Aware Reinforcement Learning through Optimal Transport Theory—0
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems—0
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Benchmark Results

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