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 1811–1820 of 15113 papers

TitleStatusHype
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot NavigationCode1
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementCode2
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
FOSP: Fine-tuning Offline Safe Policy through World Models—0
Simplifying Deep Temporal Difference LearningCode3
Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based ShieldingCode0
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks—0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Robust Decision Transformer: Tackling Data Corruption in Offline RL via Sequence Modeling—0
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Benchmark Results

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