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 7651–7675 of 15113 papers

TitleStatusHype
Global Convergence of the ODE Limit for Online Actor-Critic Algorithms in Reinforcement Learning—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Goal-conditioned Batch Reinforcement Learning for Rotation Invariant Locomotion—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Goal-conditioned Imitation Learning—0
Goal-conditioned Offline Reinforcement Learning through State Space Partitioning—0
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary—0
Goal-Conditioned Reinforcement Learning with Imagined Subgoals—0
Goal-directed Generation of Discrete Structures with Conditional Generative Models—0
Goal-Directed Planning by Reinforcement Learning and Active Inference—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—0
Goal-Driven Sequential Data Abstraction—0
Goal-oriented Dialogue Policy Learning from Failures—0
Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning—0
Goal-oriented Trajectories for Efficient Exploration—0
Goal-Oriented Visual Question Generation via Intermediate Rewards—0
Goal-Space Planning with Subgoal Models—0
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning—0
Go-Blend behavior and affect—0
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning—0
Going Beyond Linear RL: Sample Efficient Neural Function Approximation—0
Good Actions Succeed, Bad Actions Generalize: A Case Study on Why RL Generalizes Better—0
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach—0
Show:102550
← PrevPage 307 of 605Next →

Benchmark Results

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