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 13401–13450 of 15113 papers

TitleStatusHype
Investigating Enactive Learning for Autonomous Intelligent Agents—0
Continual State Representation Learning for Reinforcement Learning using Generative Replay—0
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning—0
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access—0
Multi-agent Deep Reinforcement Learning for Zero Energy Communities—0
SFV: Reinforcement Learning of Physical Skills from VideosCode0
Reinforcement Evolutionary Learning Method for self-learning—0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
PPO-CMA: Proximal Policy Optimization with Covariance Matrix AdaptationCode0
MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement LearningCode0
Learning Scheduling Algorithms for Data Processing ClustersCode0
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks—0
Comparison of Reinforcement Learning algorithms applied to the Cart Pole problemCode0
Efficient Dialog Policy Learning via Positive Memory RetentionCode0
Energy-Based Hindsight Experience PrioritizationCode0
EMI: Exploration with Mutual InformationCode0
Near-Optimal Representation Learning for Hierarchical Reinforcement LearningCode0
The Dreaming Variational Autoencoder for Reinforcement Learning EnvironmentsCode0
Reinforcement Learning with Perturbed RewardsCode0
Prediction Improves Simultaneous Neural Machine Translation—0
SmartChoices: Hybridizing Programming and Machine Learning—0
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning—0
Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning—0
A Teacher-Student Framework for Maintainable Dialog Manager—0
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—0
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain—0
Adaptive Multi-pass Decoder for Neural Machine Translation—0
Automatic Poetry Generation with Mutual Reinforcement Learning—0
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules—0
Deep Quality-Value (DQV) LearningCode0
Few-Shot Goal Inference for Visuomotor Learning and Planning—0
Learning to Perform Local Rewriting for Combinatorial OptimizationCode0
Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement LearningCode0
Reinforcement Learning in R—0
M^3RL: Mind-aware Multi-agent Management Reinforcement LearningCode0
Generalization and Regularization in DQNCode0
Direct optimization of F-measure for retrieval-based personal question answering—0
Robot Representation and Reasoning with Knowledge from Reinforcement Learning—0
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning—0
Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning—0
Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
Where Off-Policy Deep Reinforcement Learning Fails—0
Successor Options : An Option Discovery Algorithm for Reinforcement Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
Policy Generalization In Capacity-Limited Reinforcement Learning—0
Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning—0
Unsupervised Exploration with Deep Model-Based Reinforcement Learning—0
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces—0
COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS—0
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Benchmark Results

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