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 4651–4675 of 15113 papers

TitleStatusHype
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms—0
An Energy-Saving Snake Locomotion Gait Policy Obtained Using Deep Reinforcement Learning—0
An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Large-Scale Recommender Systems—0
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning—0
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations—0
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS—0
A New Approach for Resource Scheduling with Deep Reinforcement Learning—0
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System—0
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System—0
A new convergent variant of Q-learning with linear function approximation—0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
A New Framework for Query Efficient Active Imitation Learning—0
A New Interpretation of the Certainty-Equivalence Approach for PAC Reinforcement Learning with a Generative Model—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
A new Potential-Based Reward Shaping for Reinforcement Learning Agent—0
A new Reinforcement Learning framework to discover natural flavor molecules—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
A new soft computing method for integration of expert's knowledge in reinforcement learn-ing problems—0
A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting—0
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation—0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
An Experimental Design Perspective on Exploration in Reinforcement Learning—0
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap—0
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Benchmark Results

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