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 12401–12450 of 15113 papers

TitleStatusHype
Show Us the Way: Learning to Manage Dialog from Demonstrations—0
Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning—0
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning—0
Signal Temporal Logic Neural Predictive Control—0
Sign and Relevance Learning—0
Sim2real for Reinforcement Learning Driven Next Generation Networks—0
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL)—0
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning—0
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks—0
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning—0
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization—0
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning—0
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning—0
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective—0
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees—0
Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads—0
Sim-to-Real Robot Learning from Pixels with Progressive Nets—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine—0
Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning—0
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection—0
Simulating Coverage Path Planning with Roomba—0
Simulating multi-exit evacuation using deep reinforcement learning—0
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues—0
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction—0
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
Simultaneously Updating All Persistence Values in Reinforcement Learning—0
Simultaneous Perturbation Algorithms for Batch Off-Policy Search—0
Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning—0
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning—0
Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics—0
Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies—0
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial—0
Single Cell Training on Architecture Search for Image Denoising—0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence—0
Single-Shot Pruning for Offline Reinforcement Learning—0
Data-Incremental Continual Offline Reinforcement Learning—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
STEEL: Singularity-aware Reinforcement Learning—0
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems—0
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Benchmark Results

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