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 11801–11825 of 15113 papers

TitleStatusHype
Learning from Trajectories via Subgoal DiscoveryCode0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Neural Topic Model with Reinforcement Learning—0
Situated GAIL: Multitask imitation using task-conditioned adversarial inverse reinforcement learning—0
Positive-Unlabeled Reward Learning—0
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
Generalized Speedy Q-learningCode0
Generating Formality-Tuned Summaries Using Input-Dependent Rewards—0
Exploring Diverse Expressions for Paraphrase Generation—0
Frequentist Regret Bounds for Randomized Least-Squares Value IterationCode0
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances—0
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning—0
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference—0
Explicit Explore-Exploit Algorithms in Continuous State SpacesCode0
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension—0
Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven DialogueCode0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
Hierarchical Expert Networks for Meta-Learning—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
RLINK: Deep Reinforcement Learning for User Identity Linkage—0
RBED: Reward Based Epsilon Decay—0
Policy Continuation with Hindsight Inverse DynamicsCode0
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture—0
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Benchmark Results

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