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 5976–6000 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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