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 10701–10750 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving—0
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems—0
Zero-shot Text Classification via Reinforced Self-training—0
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location—0
Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning—0
Sequential Transfer in Reinforcement Learning with a Generative Model—0
Semantic Guidance of Dialogue Generation with Reinforcement Learning—0
Developing cooperative policies for multi-stage tasks—0
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
Interaction-limited Inverse Reinforcement Learning—0
Falsification-Based Robust Adversarial Reinforcement Learning—0
Convex Regularization in Monte-Carlo Tree Search—0
Composing Elementary Discourse Units in Abstractive Summarization—0
Adaptive Discretization for Model-Based Reinforcement LearningCode0
Group Equivariant Deep Reinforcement LearningCode0
Dynamic Regret of Policy Optimization in Non-stationary Environments—0
Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness—0
Deep Feature Space: A Geometrical PerspectiveCode0
Enforcing Almost-Sure Reachability in POMDPsCode0
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback—0
Testing match-3 video games with Deep Reinforcement Learning—0
Model-based Reinforcement Learning: A Survey—0
Using Reinforcement Learning to Herd a Robotic Swarm to a Target Distribution—0
Towards Learning-automation IoT Attack Detection through Reinforcement Learning—0
Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper—0
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids—0
Extracting Latent State Representations with Linear Dynamics from Rich Observations—0
Empirically Verifying Hypotheses Using Reinforcement Learning—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning—0
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon—0
Learning predictive representations in autonomous driving to improve deep reinforcement learning—0
Approximating Euclidean by Imprecise Markov Decision Processes—0
A Unifying Framework for Reinforcement Learning and Planning—0
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning—0
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning—0
Policy-GNN: Aggregation Optimization for Graph Neural NetworksCode0
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning—0
Newton-type Methods for Minimax OptimizationCode0
Reinforcement Learning and its Connections with Neuroscience and Psychology—0
Some approaches used to overcome overestimation in Deep Reinforcement Learning algorithms—0
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings—0
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain ClassifiersCode0
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes—0
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement LearningCode0
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario—0
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms—0
A differential Hebbian framework for biologically-plausible motor control—0
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Benchmark Results

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