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 951–975 of 15113 papers

TitleStatusHype
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic ControlCode1
Hierarchical clustering in particle physics through reinforcement learningCode1
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban EnvironmentsCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric VehiclesCode1
HIQL: Offline Goal-Conditioned RL with Latent States as ActionsCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure AgentsCode1
Discrete Codebook World Models for Continuous ControlCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
Hybrid Inverse Reinforcement LearningCode1
HyperDQN: A Randomized Exploration Method for Deep Reinforcement LearningCode1
HyperNCA: Growing Developmental Networks with Neural Cellular AutomataCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement LearningCode1
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Benchmark Results

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