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 7751–7775 of 15113 papers

TitleStatusHype
Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic SystemsCode1
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization—0
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets—0
Reinforcement Learning for Education: Opportunities and Challenges—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable—0
High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging—0
A Reinforcement Learning Environment for Mathematical Reasoning via Program SynthesisCode1
NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming—0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning—0
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided ExplorationCode0
Safer Reinforcement Learning through Transferable Instinct NetworksCode0
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks—0
QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning—0
Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning—0
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization—0
Surgical Instruction Generation with TransformersCode1
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot—0
Centralized Model and Exploration Policy for Multi-Agent RLCode0
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments—0
Going Beyond Linear RL: Sample Efficient Neural Function Approximation—0
Deep Adaptive Multi-Intention Inverse Reinforcement LearningCode0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning—0
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Benchmark Results

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