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 10301–10325 of 15113 papers

TitleStatusHype
Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper—0
Extracting Latent State Representations with Linear Dynamics from Rich Observations—0
Towards Learning-automation IoT Attack Detection through Reinforcement Learning—0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution—0
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEsCode1
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning—0
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon—0
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning—0
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning—0
What can I do here? A Theory of Affordances in Reinforcement LearningCode1
Online 3D Bin Packing with Constrained Deep Reinforcement LearningCode1
Policy-GNN: Aggregation Optimization for Graph Neural NetworksCode0
Approximating Euclidean by Imprecise Markov Decision Processes—0
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning—0
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data AugmentationCode1
A Unifying Framework for Reinforcement Learning and Planning—0
Learning predictive representations in autonomous driving to improve deep reinforcement learning—0
Critic Regularized RegressionCode1
Intrinsic Reward Driven Imitation Learning via Generative ModelCode1
Reinforcement Learning and its Connections with Neuroscience and Psychology—0
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings—0
Some approaches used to overcome overestimation in Deep Reinforcement Learning algorithms—0
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Benchmark Results

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