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 6501–6525 of 15113 papers

TitleStatusHype
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Distal Explanations for Model-free Explainable Reinforcement Learning—0
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers—0
Distillation Strategies for Proximal Policy Optimization—0
Distilled Agent DQN for Provable Adversarial Robustness—0
Distilled Domain Randomization—0
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents—0
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression—0
Distilling the Implicit Multi-Branch Structure in LLMs' Reasoning via Reinforcement Learning—0
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning—0
Distral: Robust Multitask Reinforcement Learning—0
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks—0
Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping—0
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
Distributed Deep Q-Learning—0
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox—0
Distributed Deep Reinforcement Learning: An Overview—0
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids—0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing—0
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Benchmark Results

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