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 14201–14250 of 15113 papers

TitleStatusHype
Online Monotone Games—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Asymmetric Actor Critic for Image-Based Robot Learning—0
The Effects of Memory Replay in Reinforcement LearningCode0
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning—0
Stochastic Variance Reduction for Policy Gradient Estimation—0
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
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
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations—0
On- and Off-Policy Monotonic Policy Improvement—0
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis—0
Rainbow: Combining Improvements in Deep Reinforcement LearningCode3
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
Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings—0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot NavigationCode0
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and DemonstrationsCode1
Deep TAMER: Interactive Agent Shaping in High-Dimensional State SpacesCode0
Overcoming Exploration in Reinforcement Learning with DemonstrationsCode0
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement LearningCode0
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning—0
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks—0
Cold-Start Reinforcement Learning with Softmax Policy GradientCode0
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
MDP environments for the OpenAI GymCode0
Object-oriented Neural Programming (OONP) for Document Understanding—0
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following—0
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars—0
Inverse Reinforcement Learning with Conditional Choice Probabilities—0
Multiqubit and multilevel quantum reinforcement learning with quantum technologies—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
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning—0
Neural Optimizer Search with Reinforcement LearningCode0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization—0
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks—0
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement LearningCode0
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision ProcessesCode0
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning—0
Deep Reinforcement Learning that MattersCode0
Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Models—0
Guided Deep Reinforcement Learning for Swarm SystemsCode0
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Benchmark Results

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