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

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Showing 6976–7000 of 15113 papers

TitleStatusHype
Exploration in Deep Reinforcement Learning: A Survey—0
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach—0
Learning user-defined sub-goals using memory editing in reinforcement learning—0
[CASPI] Causal-aware Safe Policy Improvement for Task-oriented Dialogue—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation—0
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach—0
Reinforced Cross-modal Alignment for Radiology Report GenerationCode0
Processing Network Controls via Deep Reinforcement Learning—0
Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision ProcessesCode0
Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery—0
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning ApproachCode0
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery—0
Toward Compositional Generalization in Object-Oriented World Modeling—0
RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources—0
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems—0
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning—0
BATS: Best Action Trajectory Stitching—0
Learning Eco-Driving Strategies at Signalized Intersections—0
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search—0
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learningCode0
Toward Policy Explanations for Multi-Agent Reinforcement LearningCode0
Skill-based Meta-Reinforcement Learning—0
Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement LearningCode0
Task-Induced Representation Learning—0
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Benchmark Results

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