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 3001–3025 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning with Dual Robustness—0
Efficient Reinforcement Learning for Jumping MonopodsCode0
Investigating the Impact of Action Representations in Policy Gradient Algorithms—0
Reasoning with Latent Diffusion in Offline Reinforcement LearningCode1
Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures—0
Risk-Aware Reinforcement Learning through Optimal Transport Theory—0
Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement LearningCode1
Representation Learning in Low-rank Slate-based Recommender Systems—0
Signal Temporal Logic Neural Predictive Control—0
Verifiable Reinforcement Learning Systems via Compositionality—0
Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective—0
Compositional Learning of Visually-Grounded Concepts Using ReinforcementCode0
Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control—0
A State Representation for Diminishing Rewards—0
Deep Reinforcement Learning from Hierarchical Preference DesignCode0
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation—0
Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal ModelsCode2
Dialog Action-Aware Transformer for Dialog Policy Learning—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
Model-based Offline Policy Optimization with Adversarial NetworkCode0
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained ModelsCode0
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance—0
INTAGS: Interactive Agent-Guided Simulation—0
Marginalized Importance Sampling for Off-Environment Policy Evaluation—0
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy—0
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Benchmark Results

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