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

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Showing 13351–13400 of 15113 papers

TitleStatusHype
Assessing Generalization in Deep Reinforcement LearningCode0
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable FeedbackCode0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
Learn to Steer through Deep Reinforcement LearningCode0
Multi-Agent Common Knowledge Reinforcement LearningCode0
Transfer of Deep Reactive Policies for MDP PlanningCode0
Stability-certified reinforcement learning: A control-theoretic perspective—0
Neural Modular Control for Embodied Question AnsweringCode0
Empirical Evaluation of Contextual Policy Search with a Comparison-based Surrogate Model and Active Covariance Matrix Adaptation—0
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning—0
Inverse reinforcement learning for video gamesCode0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions—0
Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space—0
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients—0
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Graph Convolutional Reinforcement LearningCode0
Multi-Agent Actor-Critic with Generative Cooperative Policy Network—0
Risk-Sensitive Reinforcement Learning via Policy Gradient Search—0
RLgraph: Modular Computation Graphs for Deep Reinforcement LearningCode0
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments—0
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents—0
Safe Reinforcement Learning with Model Uncertainty Estimates—0
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey—0
Fast deep reinforcement learning using online adjustments from the pastCode0
Finding the best design parameters for optical nanostructures using reinforcement learning—0
At Human Speed: Deep Reinforcement Learning with Action Delay—0
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement LearningCode0
The Concept of Criticality in Reinforcement Learning—0
Reinforcement Learning Decoders for Fault-Tolerant Quantum ComputationCode0
Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms—0
Multi-Stage Reinforcement Learning For Object DetectionCode0
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference LearningCode0
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningCode0
Deep Transfer Reinforcement Learning for Text SummarizationCode0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost—0
Two Can Play That Game: An Adversarial Evaluation of a Cyber-alert Inspection System—0
Optimal Hierarchical Learning Path Design with Reinforcement Learning—0
Bayesian Inference of Self-intention Attributed by Observer—0
A Survey and Critique of Multiagent Deep Reinforcement Learning—0
Empowerment-driven Exploration using Mutual Information EstimationCode0
Adversarial Text Generation Without Reinforcement Learning—0
Closed-form approximations in multi-asset market making—0
The Laplacian in RL: Learning Representations with Efficient Approximations—0
Reinforcement Learning for Improving Agent DesignCode0
Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City ServicesCode0
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges—0
Discovering General-Purpose Active Learning StrategiesCode0
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem—0
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Benchmark Results

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