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 8226–8250 of 15113 papers

TitleStatusHype
No-regret Exploration in Shuffle Private Reinforcement Learning—0
No-Regret Reinforcement Learning in Smooth MDPs—0
No-Regret Reinforcement Learning with Heavy-Tailed Rewards—0
Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning—0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
NoRML: No-Reward Meta Learning—0
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning—0
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds—0
Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators—0
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks—0
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty—0
Now I Remember! Episodic Memory For Reinforcement Learning—0
NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration—0
Nuclear Microreactor Control with Deep Reinforcement Learning—0
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning—0
Object-Category Aware Reinforcement Learning—0
Object Exchangeability in Reinforcement Learning: Extended Abstract—0
Objective-aware Traffic Simulation via Inverse Reinforcement Learning—0
Object-oriented Neural Programming (OONP) for Document Understanding—0
Object-sensitive Deep Reinforcement Learning—0
Observational Learning by Reinforcement Learning—0
Observational Overfitting in Reinforcement Learning—0
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives—0
Observe and Look Further: Achieving Consistent Performance on Atari—0
Observed Adversaries in Deep Reinforcement Learning—0
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Benchmark Results

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