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 9226–9250 of 15113 papers

TitleStatusHype
Demystify Painting with RL—0
Deploying Reinforcement Learning in Water Transport—0
Automatic Source Code Summarization via Reinforcement Learning—0
Learning Mobile Robot Navigation in the Dense Crowd with Deep Reinforcement Learning—0
A case for new neural network smoothness constraints—0
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
Policy Gradient RL Algorithms as Directed Acyclic GraphsCode1
Sim-to-real reinforcement learning applied to end-to-end vehicle controlCode1
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
Learning for MPC with Stability & Safety Guarantees—0
Active Hierarchical Imitation and Reinforcement Learning—0
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability—0
Evolutionary learning of interpretable decision treesCode0
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Tutoring Reinforcement Learning via Feedback Control—0
Semi-supervised reward learning for offline reinforcement learning—0
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks—0
Regularizing Action Policies for Smooth Control with Reinforcement Learning—0
OPAC: Opportunistic Actor-Critic—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning—0
Flatland-RL : Multi-Agent Reinforcement Learning on Trains—0
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
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Benchmark Results

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