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 2651–2675 of 15113 papers

TitleStatusHype
Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing TasksCode0
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearnCode0
Attention-based Curiosity-driven Exploration in Deep Reinforcement LearningCode0
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement LearningCode0
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement LearningCode0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
Interactive Query-Assisted Summarization via Deep Reinforcement LearningCode0
Interval timing in deep reinforcement learning agentsCode0
Adaptive Auxiliary Task Weighting for Reinforcement LearningCode0
Interactive Learning from Activity DescriptionCode0
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement LearningCode0
A Tree Search Algorithm for Sequence LabelingCode0
AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience ReplayCode0
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement LearningCode0
Intelligent Trainer for Model-Based Reinforcement LearningCode0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
A Tour of Reinforcement Learning: The View from Continuous ControlCode0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement LearningCode0
A Threshold-based Scheme for Reinforcement Learning in Neural NetworksCode0
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
Insights From the NeurIPS 2021 NetHack ChallengeCode0
Instance based Generalization in Reinforcement LearningCode0
Input Convex Neural NetworksCode0
Show:102550
← PrevPage 107 of 605Next →

Benchmark Results

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