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 6801–6850 of 15113 papers

TitleStatusHype
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing—0
Causal policy ranking—0
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills—0
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
On Effective Scheduling of Model-based Reinforcement LearningCode1
The Partially Observable History Process—0
VisualEnv: visual Gym environments with Blender—0
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement LearningCode0
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control—0
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning—0
Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework—0
AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive CrossbarsCode0
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in RoboticsCode0
Optimism and Delays in Episodic Reinforcement Learning—0
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization—0
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning—0
Reinforcement Learning of Self Enhancing Camera Image and Signal ProcessingCode0
Versatile Inverse Reinforcement Learning via Cumulative Rewards—0
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Free Will Belief as a consequence of Model-based Reinforcement Learning—0
Explicit Explore, Exploit, or Escape (E^4): near-optimal safety-constrained reinforcement learning in polynomial time—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning—0
Where to Look: A Unified Attention Model for Visual Recognition with Reinforcement Learning—0
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