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 14251–14300 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning with Quantile RegressionCode0
Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task LearningCode0
Learning Approximate Stochastic Transition ModelsCode0
Accelerated Reinforcement Learning—0
Exploiting generalization in the subspaces for faster model-based learning—0
Insulin Regimen ML-based control for T2DM patients—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Online Monotone Games—0
The Effects of Memory Replay in Reinforcement LearningCode0
Asymmetric Actor Critic for Image-Based Robot Learning—0
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning—0
Stochastic Variance Reduction for Policy Gradient Estimation—0
Manifold Regularization for Kernelized LSTD—0
Is Epicurus the father of Reinforcement Learning?—0
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning—0
On- and Off-Policy Monotonic Policy Improvement—0
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations—0
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis—0
Exploration in Feature Space for Reinforcement Learning—0
Event Identification as a Decision Process with Non-linear Representation of Text—0
Detecting Adversarial Attacks on Neural Network Policies with Visual ForesightCode0
Attention-Aware Deep Reinforcement Learning for Video Face Recognition—0
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning—0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot NavigationCode0
Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings—0
Deep TAMER: Interactive Agent Shaping in High-Dimensional State SpacesCode0
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement LearningCode0
Overcoming Exploration in Reinforcement Learning with DemonstrationsCode0
Cold-Start Reinforcement Learning with Softmax Policy GradientCode0
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks—0
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning—0
Object-oriented Neural Programming (OONP) for Document Understanding—0
MDP environments for the OpenAI GymCode0
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning—0
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars—0
Inverse Reinforcement Learning with Conditional Choice Probabilities—0
On overfitting and asymptotic bias in batch reinforcement learning with partial observability—0
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World—0
Multiqubit and multilevel quantum reinforcement learning with quantum technologies—0
Neural Optimizer Search with Reinforcement LearningCode0
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks—0
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization—0
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement LearningCode0
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning—0
Deep Reinforcement Learning that MattersCode0
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision ProcessesCode0
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Benchmark Results

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