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 2926–2950 of 15113 papers

TitleStatusHype
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
A* Tree Search for Portfolio Management—0
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules—0
Constrained Reinforcement Learning Has Zero Duality Gap—0
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces—0
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning—0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning—0
A Hybrid PAC Reinforcement Learning Algorithm—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
Adaptive Aggregation for Safety-Critical Control—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
INTAGS: Interactive Agent-Guided Simulation—0
At Human Speed: Deep Reinforcement Learning with Action Delay—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
A Fast Convergence Theory for Offline Decision Making—0
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search—0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning—0
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Benchmark Results

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