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 2311–2320 of 15113 papers

TitleStatusHype
Robots and Children that Learn Together : Improving Knowledge Retention by Teaching Peer-Like Interactive Robots—0
Accelerating Residual Reinforcement Learning with Uncertainty Estimation—0
Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking—0
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using SparsityCode0
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy EvaluationCode0
Learning Dexterous Object Handover—0
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations—0
VRAIL: Vectorized Reward-based Attribution for Interpretable Learning—0
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation—0
Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks—0
Show:102550
← PrevPage 232 of 1512Next →

Benchmark Results

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