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 14801–14850 of 15113 papers

TitleStatusHype
Model-Free Imitation Learning with Policy Optimization—0
A PAC RL Algorithm for Episodic POMDPs—0
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural NetworksCode0
Learning to Communicate with Deep Multi-Agent Reinforcement LearningCode0
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition—0
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering—0
A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients—0
Optimizing human-interpretable dialog management policy using Genetic Algorithm—0
Avoiding Wireheading with Value Reinforcement Learning—0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement LearningCode0
Classifying Options for Deep Reinforcement Learning—0
Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning—0
Using Reinforcement Learning to Validate Empirical Game-Theoretic Analysis: A Continuous Double Auction Study—0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics—0
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer—0
A statistical learning strategy for closed-loop control of fluid flows—0
Data-Efficient Off-Policy Policy Evaluation for Reinforcement LearningCode0
Reinforcement learning based local search for grouping problems: A case study on graph coloring—0
Algorithms for Batch Hierarchical Reinforcement Learning—0
Negative Learning Rates and P-Learning—0
Improving Information Extraction by Acquiring External Evidence with Reinforcement LearningCode0
Fully Convolutional Attention Networks for Fine-Grained Recognition—0
Adaptive Parameter Selection in Evolutionary Algorithms by Reinforcement Learning with Dynamic Discretization of Parameter Range—0
Feature Selection as a Multiagent Coordination Problem—0
Exploratory Gradient Boosting for Reinforcement Learning in Complex DomainsCode0
A Signaling Game Approach to Databases Querying and Interaction—0
Hierarchical Linearly-Solvable Markov Decision Problems—0
Differentially Private Policy Evaluation—0
Learning Shared Representations in Multi-task Reinforcement Learning—0
Hierarchical Decision Making In Electricity Grid Management—0
Reinforcement Learning of POMDPs using Spectral Methods—0
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural NetworksCode0
Thompson Sampling is Asymptotically Optimal in General Environments—0
Meta-learning within Projective Simulation—0
Learning values across many orders of magnitude—0
Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models—0
Inverse Reinforcement Learning in Swarm Systems—0
POMDP-lite for Robust Robot Planning under Uncertainty—0
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3—0
Deep Exploration via Bootstrapped DQNCode0
Value Iteration NetworksCode0
PAC Reinforcement Learning with Rich Observations—0
Graying the black box: Understanding DQNs—0
Data-Efficient Reinforcement Learning in Continuous-State POMDPs—0
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks—0
Active Information Acquisition—0
Quantum machine learning with glow for episodic tasks and decision games—0
Towards Resolving Unidentifiability in Inverse Reinforcement Learning—0
SimpleDS: A Simple Deep Reinforcement Learning Dialogue SystemCode0
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Benchmark Results

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