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 3801–3850 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
A Model-Based Reinforcement Learning Approach for PID Design—0
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation—0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey—0
Deep Reinforcement Learning with Decorrelation—0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Deep Reinforcement Learning with Adjustments—0
Avoiding Wireheading with Value Reinforcement Learning—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Correlation Priors for Reinforcement Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Deep Reinforcement Learning with Explicit Context Representation—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model—0
Assume-Guarantee Reinforcement Learning—0
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing—0
Deep Reinforcement Learning with Linear Quadratic Regulator Regions—0
Deep Reinforcement Learning With Macro-Actions—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft—0
Deep Reinforcement Learning with Plasticity Injection—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
A Generative Framework for Simultaneous Machine Translation—0
Deep reinforcement learning with relational inductive biases—0
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning—0
Deep Reinforcement Learning with Robust and Smooth Policy—0
Deep Reinforcement Learning with Smooth Policy—0
Deep Reinforcement Learning with Spiking Q-learning—0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications—0
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments—0
Deep Reinforcement Learning with Surrogate Agent-Environment Interface—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
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Benchmark Results

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