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 4141–4150 of 15113 papers

TitleStatusHype
Towards Hardware-Specific Automatic Compression of Neural Networks—0
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies—0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partial observability—0
Emergent Behaviors in Multi-Agent Target Acquisition—0
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning—0
Residual Policy Learning for Powertrain Control—0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management—0
Reinforcement Learning in System Identification—0
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning—0
Show:102550
← PrevPage 415 of 1512Next →

Benchmark Results

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