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 2126–2150 of 15113 papers

TitleStatusHype
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning—0
Proximal Curriculum with Task Correlations for Deep Reinforcement LearningCode0
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
Model-based reinforcement learning for protein backbone design—0
Learning Optimal Deterministic Policies with Stochastic Policy Gradients—0
Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk—0
Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient ManipulationCode5
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation—0
Reinforcement Learning-Guided Semi-Supervised Learning—0
Constrained Reinforcement Learning Under Model Mismatch—0
Tabular and Deep Reinforcement Learning for Gittins Index—0
FLAME: Factuality-Aware Alignment for Large Language Models—0
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks—0
Learning Force Control for Legged Manipulation—0
Queue-based Eco-Driving at Roundabouts with Reinforcement Learning—0
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPOCode1
Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning—0
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning—0
Towards Generalist Robot Learning from Internet Video: A Survey—0
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement LearningCode0
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Benchmark Results

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