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 14101–14150 of 15113 papers

TitleStatusHype
Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control—0
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning—0
Learning an Embedding Space for Transferable Robot Skills—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Avoiding Catastrophic States with Intrinsic Fear—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
NerveNet: Learning Structured Policy with Graph Neural NetworksCode0
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines—0
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus—0
Neural Task Graph Execution—0
Universal Agent for Disentangling Environments and Tasks—0
Model-based imitation learning from state trajectories—0
Predicting Multiple Actions for Stochastic Continuous Control—0
Neuron as an Agent—0
Using Deep Reinforcement Learning to Generate Rationales for Molecules—0
Residual Loss Prediction: Reinforcement Learning With No Incremental FeedbackCode0
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning—0
Reward Estimation via State Prediction—0
Now I Remember! Episodic Memory For Reinforcement Learning—0
Learning Structural Weight Uncertainty for Sequential Decision-MakingCode0
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness RewardCode0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation—0
Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memoryCode0
Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention—0
Consensus-based Sequence Training for Video Captioning—0
A short variational proof of equivalence between policy gradients and soft Q learning—0
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator—0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning—0
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition—0
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning—0
Pseudorehearsal in actor-critic agents with neural network function approximation—0
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning—0
Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning—0
On Wasserstein Reinforcement Learning and the Fokker-Planck equation—0
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent—0
ES Is More Than Just a Traditional Finite-Difference Approximator—0
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking AgentsCode0
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement LearningCode0
Integral Equations and Machine Learning—0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars—0
Occam's razor is insufficient to infer the preferences of irrational agents—0
Hierarchical Text Generation and Planning for Strategic DialogueCode0
Differentiable lower bound for expected BLEU scoreCode0
Inverse Reinforcement Learning for Marketing—0
QLBS: Q-Learner in the Black-Scholes(-Merton) WorldsCode0
Multi-focus Attention Network for Efficient Deep Reinforcement Learning—0
Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning—0
Deep Reinforcement Learning Boosted by External Knowledge—0
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Benchmark Results

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