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 10701–10725 of 15113 papers

TitleStatusHype
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning—0
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds—0
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems—0
Explore Reinforced: Equilibrium Approximation with Reinforcement Learning—0
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning—0
Explore with Dynamic Map: Graph Structured Reinforcement Learning—0
Exploring applications of deep reinforcement learning for real-world autonomous driving systems—0
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning—0
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design—0
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
Show:102550
← PrevPage 429 of 605Next →

Benchmark Results

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