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 1461–1470 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
Deep Reinforcement Learning for Active High Frequency TradingCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Deep Reinforcement Learning for Adaptive Exploration of Unknown EnvironmentsCode1
Federated Reinforcement Learning with Environment HeterogeneityCode1
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Show:102550
← PrevPage 147 of 1512Next →

Benchmark Results

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