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 1801–1825 of 15113 papers

TitleStatusHype
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning—0
Structural Design Through Reinforcement LearningCode0
Continuous Control with Coarse-to-fine Reinforcement Learning—0
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing—0
Preference-Guided Reinforcement Learning for Efficient ExplorationCode0
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning—0
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks—0
On Bellman equations for continuous-time policy evaluation I: discretization and approximation—0
Periodic agent-state based Q-learning for POMDPs—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 Reinforcement Learning-based Network Intrusion Detection System—0
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
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks—0
Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based ShieldingCode0
Hindsight Preference Learning for Offline Preference-based Reinforcement LearningCode1
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Robust Decision Transformer: Tackling Data Corruption in Offline RL via Sequence Modeling—0
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language ModelsCode0
ROER: Regularized Optimal Experience ReplayCode0
Craftium: An Extensible Framework for Creating Reinforcement Learning EnvironmentsCode2
RobocupGym: A challenging continuous control benchmark in RobocupCode1
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Benchmark Results

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