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 6426–6450 of 15113 papers

TitleStatusHype
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning—0
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning—0
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum—0
Techniques for Automated Machine Learning—0
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning—0
Temporal Abstraction in Reinforcement Learning with the Successor Representation—0
Temporal-adaptive Hierarchical Reinforcement Learning—0
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification—0
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation—0
Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Temporal Difference Learning with Experience Replay—0
Temporal Difference Models: Model-Free Deep RL for Model-Based Control—0
Temporal Difference Weighted Ensemble For Reinforcement Learning—0
Temporal-Differential Learning in Continuous Environments—0
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning—0
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking—0
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks—0
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions—0
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?—0
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning—0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning—0
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Benchmark Results

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