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 4951–4975 of 15113 papers

TitleStatusHype
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation—0
Model-Based Reinforcement Learning with Multi-Task Offline PretrainingCode0
Mildly Constrained Evaluation Policy for Offline Reinforcement LearningCode0
Boosting Offline Reinforcement Learning with Action Preference Query—0
CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments—0
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control—0
A General Perspective on Objectives of Reinforcement Learning—0
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems—0
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous DrivingCode0
Survival Instinct in Offline Reinforcement Learning—0
Cycle Consistency Driven Object Discovery—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction—0
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations—0
An Architecture for Deploying Reinforcement Learning in Industrial Environments—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
Hyperparameters in Reinforcement Learning and How To Tune Them—0
Learning to Stabilize Online Reinforcement Learning in Unbounded State SpacesCode0
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space—0
Non-stationary Reinforcement Learning under General Function Approximation—0
Normalization Enhances Generalization in Visual Reinforcement LearningCode0
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning—0
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding—0
Identifiability and Generalizability in Constrained Inverse Reinforcement LearningCode0
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Benchmark Results

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