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 7226–7250 of 15113 papers

TitleStatusHype
Exploring Deep Reinforcement Learning for Holistic Smart Building Control—0
Exploring Diverse Expressions for Paraphrase Generation—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach—0
Exploring Hierarchy-Aware Inverse Reinforcement Learning—0
Exploring market power using deep reinforcement learning for intelligent bidding strategies—0
Exploring More When It Needs in Deep Reinforcement Learning—0
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey—0
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks—0
Exploring the Benefits of Teams in Multiagent Learning—0
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning—0
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study—0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
Exploring Transferability of Perturbations in Deep Reinforcement Learning—0
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning—0
Exponential Hardness of Reinforcement Learning with Linear Function Approximation—0
Exponential improvements for quantum-accessible reinforcement learning—0
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL—0
Exponentially Weighted Imitation Learning for Batched Historical Data—0
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain—0
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning—0
A Tractable Inference Perspective of Offline RL—0
Expressiveness in Deep Reinforcement Learning—0
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Benchmark Results

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