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 2191–2200 of 15113 papers

TitleStatusHype
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Discrete Codebook World Models for Continuous ControlCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Show:102550
← PrevPage 220 of 1512Next →

Benchmark Results

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