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 12201–12250 of 15113 papers

TitleStatusHype
Scalable Reinforcement Learning for Virtual Machine Scheduling—0
Scalable Reinforcement Learning for Multi-Agent Networked Systems—0
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems—0
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning—0
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis—0
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning—0
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control—0
Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning—0
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning—0
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning—0
ScaleViz: Scaling Visualization Recommendation Models on Large Data—0
Scaling active inference—0
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing—0
Scaling Goal-based Exploration via Pruning Proto-goals—0
Scaling Intelligent Agents in Combat Simulations for Wargaming—0
Scaling Laws for Reward Model Overoptimization—0
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms—0
Scaling laws for single-agent reinforcement learning—0
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Scaling Offline RL via Efficient and Expressive Shortcut Models—0
Scaling RL to Long Videos—0
Scaling shared model governance via model splitting—0
Scaling Test-Time Compute Without Verification or RL is Suboptimal—0
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph—0
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training—0
Scaling Up Robust MDPs by Reinforcement Learning—0
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions—0
Scenario-Assisted Deep Reinforcement Learning—0
Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks—0
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments—0
Schedule Earth Observation satellites with Deep Reinforcement Learning—0
ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward—0
Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning—0
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning—0
Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning—0
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning—0
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget—0
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning—0
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning—0
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models—0
SDN Flow Entry Management Using Reinforcement Learning—0
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning—0
Search-Based Testing of Reinforcement Learning—0
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Benchmark Results

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