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 3751–3775 of 15113 papers

TitleStatusHype
Multiagent Inverse Reinforcement Learning via Theory of Mind ReasoningCode0
Reinforcement Learning with Function Approximation: From Linear to Nonlinear—0
Backstepping Temporal Difference Learning—0
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
Differentiable Arbitrating in Zero-sum Markov Games—0
Safe Deep Reinforcement Learning by Verifying Task-Level Properties—0
Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)Code0
Compositionality and Bounds for Optimal Value Functions in Reinforcement Learning—0
AutoDOViz: Human-Centered Automation for Decision Optimization—0
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning—0
Generalization in Visual Reinforcement Learning with the Reward Sequence DistributionCode0
Interactive Video Corpus Moment Retrieval using Reinforcement Learning—0
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement—0
Mixed Traffic Control and Coordination from Pixels—0
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories—0
Data Driven Reward Initialization for Preference based Reinforcement Learning—0
Robot path planning using deep reinforcement learning—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning—0
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Benchmark Results

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