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 6826–6850 of 15113 papers

TitleStatusHype
Robust Deep Reinforcement Learning for Extractive Legal Summarization—0
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning—0
Improving Experience Replay through Modeling of Similar Transitions' SetsCode0
AWD3: Dynamic Reduction of the Estimation Bias—0
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
DriverGym: Democratising Reinforcement Learning for Autonomous Driving—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN—0
Two steps to risk sensitivityCode0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Resilient Consensus-based Multi-agent Reinforcement Learning with Function ApproximationCode1
User Allocation in Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
CubeTR: Learning to Solve The Rubiks Cube Using Transformers—0
Agent Spaces—0
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationCode1
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning—0
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving—0
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention—0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning—0
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Benchmark Results

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