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 3151–3175 of 15113 papers

TitleStatusHype
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Curiosity-driven reinforcement learning with homeostatic regulation—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
A Survey on Deep Reinforcement Learning for Data Processing and Analytics—0
A Survey on Dialog Management: Recent Advances and Challenges—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Agent Spaces—0
Curious iLQR: Resolving Uncertainty in Model-based RL—0
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation—0
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback—0
A Survey of Deep Reinforcement Learning in Video Games—0
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning—0
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
Curriculum-based Deep Reinforcement Learning for Quantum Control—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading—0
Agent Probing Interaction Policies—0
Curriculum goal masking for continuous deep reinforcement learning—0
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—0
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey—0
Curriculum Learning with a Progression Function—0
Curriculum Offline Imitating Learning—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
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Benchmark Results

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