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 11151–11175 of 15113 papers

TitleStatusHype
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
GraCo -- A Graph Composer for Integrated Circuits—0
Gradient-EM Bayesian Meta-learning—0
Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning—0
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction—0
Gradient Monitored Reinforcement Learning—0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Grammar and Gameplay-aligned RL for Game Description Generation with LLMs—0
Grammatical Error Correction with Neural Reinforcement Learning—0
Granger Causal Interaction Skill Chains—0
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization—0
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment—0
Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning—0
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Benchmark Results

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