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 14751–14800 of 15113 papers

TitleStatusHype
Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes—0
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Perceptual Reward Functions—0
On Lower Bounds for Regret in Reinforcement Learning—0
Posterior Sampling for Reinforcement Learning Without EpisodesCode0
Neuroevolution-Based Inverse Reinforcement Learning—0
Online Adaptation of Deep Architectures with Reinforcement Learning—0
Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context—0
Self-organization in a distributed coordination game through heuristic rules—0
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction—0
Accelerating Stochastic Composition Optimization—0
An Actor-Critic Algorithm for Sequence PredictionCode0
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint ReplayCode0
Sequential Cost-Sensitive Feature Acquisition—0
Automatic Bridge Bidding Using Deep Reinforcement Learning—0
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference LearningCode0
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?—0
Is the Bellman residual a bad proxy?—0
Unsupervised preprocessing for Tactile Data—0
Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning—0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks—0
On Reward Function for Survival—0
Successor Features for Transfer in Reinforcement Learning—0
Deep Reinforcement Learning Discovers Internal Models—0
Deep Reinforcement Learning With Macro-Actions—0
Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning—0
Model-Free Episodic ControlCode0
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit ThreadsCode0
Policy Networks with Two-Stage Training for Dialogue Systems—0
Cooperative Inverse Reinforcement LearningCode0
Face valuing: Training user interfaces with facial expressions and reinforcement learning—0
Continuously Learning Neural Dialogue Management—0
Deep Successor Reinforcement LearningCode0
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement LearningCode0
Safe and Efficient Off-Policy Reinforcement LearningCode0
Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning—0
Learning to Optimize—0
Unifying Count-Based Exploration and Intrinsic MotivationCode0
Deep Reinforcement Learning for Dialogue GenerationCode0
Deep Q-Networks for Accelerating the Training of Deep Neural Networks—0
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning—0
Difference of Convex Functions Programming Applied to Control with Expert Data—0
Reinforcement Learning for Semantic Segmentation in Indoor Scenes—0
Death and Suicide in Universal Artificial Intelligence—0
Reinforcement Learning for Visual Object Detection—0
VIME: Variational Information Maximizing ExplorationCode0
Information Theoretically Aided Reinforcement Learning for Embodied Agents—0
Control of Memory, Active Perception, and Action in Minecraft—0
Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL AgentCode0
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Benchmark Results

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