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 7851–7900 of 15113 papers

TitleStatusHype
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing—0
Deep Reinforcement Learning for Entity Alignment—0
AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive CrossbarsCode0
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in RoboticsCode0
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning—0
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization—0
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning—0
Optimism and Delays in Episodic Reinforcement Learning—0
Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework—0
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control—0
Reinforcement Learning of Self Enhancing Camera Image and Signal ProcessingCode0
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement LearningCode0
VisualEnv: visual Gym environments with Blender—0
The Partially Observable History Process—0
Versatile Inverse Reinforcement Learning via Cumulative Rewards—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Explicit Explore, Exploit, or Escape (E^4): near-optimal safety-constrained reinforcement learning in polynomial time—0
Free Will Belief as a consequence of Model-based Reinforcement Learning—0
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning—0
Where to Look: A Unified Attention Model for Visual Recognition with Reinforcement Learning—0
Robust Deep Reinforcement Learning for Extractive Legal Summarization—0
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning—0
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN—0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Two steps to risk sensitivityCode0
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
Improving Experience Replay through Modeling of Similar Transitions' SetsCode0
CubeTR: Learning to Solve The Rubiks Cube Using Transformers—0
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Agent Spaces—0
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving—0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention—0
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning—0
HARPO: Learning to Subvert Online Behavioral Advertising—0
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning—0
Safe Policy Optimization with Local Generalized Linear Function ApproximationsCode0
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods—0
Interactive Inverse Reinforcement Learning for Cooperative Games—0
Batch Reinforcement Learning from Crowds—0
Dueling RL: Reinforcement Learning with Trajectory Preferences—0
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach—0
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance—0
Automatic Goal Generation using Dynamical Distance Learning—0
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Benchmark Results

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