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 2976–3000 of 15113 papers

TitleStatusHype
A Taxonomy of Similarity Metrics for Markov Decision Processes—0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach—0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Atari games and Intel processors—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
Gamifying the Vehicle Routing Problem with Stochastic Requests—0
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound—0
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning—0
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning—0
Deep Coherent Exploration For Continuous Control—0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
Adapting World Models with Latent-State Dynamics Residuals—0
Asynchronous training of quantum reinforcement learning—0
A Hierarchical Model for Device Placement—0
Deep Binary Reinforcement Learning for Scalable Verification—0
Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks—0
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction—0
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning—0
Adapting User Interfaces with Model-based Reinforcement Learning—0
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning—0
Deep Communicating Agents for Abstractive Summarization—0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis—0
Show:102550
← PrevPage 120 of 605Next →

Benchmark Results

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