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 8101–8125 of 15113 papers

TitleStatusHype
“Other-Play” for Zero-Shot Coordination—0
OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning—0
OTTR: Off-Road Trajectory Tracking using Reinforcement Learning—0
Outcome-Constrained Large Language Models for Countering Hate Speech—0
Outcome-Driven Reinforcement Learning via Variational Inference—0
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space—0
Outline Objects using Deep Reinforcement Learning—0
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows—0
Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agents—0
Out-of-distribution generalization of internal models is correlated with reward—0
Out-of-the-box channel pruned networks—0
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model—0
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning—0
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications—0
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning—0
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning—0
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization—0
Overcoming the Spectral Bias of Neural Value Approximation—0
Over-communicate no more: Situated RL agents learn concise communication protocols—0
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning—0
Over-the-fiber Digital Predistortion Using Reinforcement Learning—0
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization—0
PAC-Bayesian Model Selection for Reinforcement Learning—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
PAC-Bayesian Randomized Value Function with Informative Prior—0
Show:102550
← PrevPage 325 of 605Next →

Benchmark Results

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