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

Showing 29513000 of 15113 papers

TitleStatusHype
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum0
A Fast Convergence Theory for Offline Decision Making0
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning0
Deep Curiosity Loops in Social Environments0
A Theory of Abstraction in Reinforcement Learning0
A Theoretical Connection Between Statistical Physics and Reinforcement Learning0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression0
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes0
A Human Mixed Strategy Approach to Deep Reinforcement Learning0
Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems0
A Tensor Network Approach to Finite Markov Decision Processes0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors0
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming0
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations0
ACDER: Augmented Curiosity-Driven Experience Replay0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability0
DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning0
A Technical Study into Small Reasoning Language Models0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization0
A Teacher-Student Framework for Maintainable Dialog Manager0
A Taxonomy of Similarity Metrics for Markov Decision Processes0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games0
Atari games and Intel processors0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning0
Gamifying the Vehicle Routing Problem with Stochastic Requests0
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound0
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning0
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning0
Deep Coherent Exploration For Continuous Control0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior0
Adapting World Models with Latent-State Dynamics Residuals0
Asynchronous training of quantum reinforcement learning0
A Hierarchical Model for Device Placement0
Deep Binary Reinforcement Learning for Scalable Verification0
Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks0
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction0
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning0
Adapting User Interfaces with Model-based Reinforcement Learning0
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning0
Deep Communicating Agents for Abstractive Summarization0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing0
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis0
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Benchmark Results

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