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 8301–8325 of 15113 papers

TitleStatusHype
^2-exploration for Reinforcement Learning—0
Superior Performance with Diversified Strategic Control in FPS Games Using General Reinforcement Learning—0
Mitigation of Adversarial Policy Imitation via Constrained Randomization of Policy (CRoP)—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning—0
Understanding and Leveraging Overparameterization in Recursive Value Estimation—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Reasoning With Hierarchical Symbols: Reclaiming Symbolic Policies For Visual Reinforcement Learning—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
Metrics Matter: A Closer Look on Self-Paced Reinforcement Learning—0
MURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization—0
Untangling Braids with Multi-agent Q-Learning—0
Pretraining for Language Conditioned Imitation with Transformers—0
Online Robust Reinforcement Learning with Model Uncertainty—0
Meta Attention For Off-Policy Actor-Critic—0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
Value Refinement Network (VRN)—0
PDQN - A Deep Reinforcement Learning Method for Planning with Long Delays: Optimization of Manufacturing Dispatching—0
Variational oracle guiding for reinforcement learning—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—0
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems—0
Safe Exploration in Linear Equality Constraint—0
State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning—0
Reinforcement Learning with Predictive Consistent Representations—0
Maximizing Ensemble Diversity in Deep Reinforcement Learning—0
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Benchmark Results

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