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 3301–3325 of 15113 papers

TitleStatusHype
State Regularized Policy Optimization on Data with Dynamics Shift—0
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous DrivingCode0
Survival Instinct in Offline Reinforcement Learning—0
A General Perspective on Objectives of Reinforcement Learning—0
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-CriticCode1
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control—0
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems—0
For SALE: State-Action Representation Learning for Deep Reinforcement LearningCode1
Cycle Consistency Driven Object Discovery—0
Learning to Stabilize Online Reinforcement Learning in Unbounded State SpacesCode0
Improving the generalizability and robustness of large-scale traffic signal control—0
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space—0
Hyperparameters in Reinforcement Learning and How To Tune Them—0
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task—0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications—0
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction—0
An Architecture for Deploying Reinforcement Learning in Industrial Environments—0
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations—0
Thought Cloning: Learning to Think while Acting by Imitating Human ThinkingCode2
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning—0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding—0
TorchRL: A data-driven decision-making library for PyTorchCode4
Normalization Enhances Generalization in Visual Reinforcement LearningCode0
Identifiability and Generalizability in Constrained Inverse Reinforcement LearningCode0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
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Benchmark Results

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