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 3926–3950 of 15113 papers

TitleStatusHype
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement LearningCode0
Flight Controller Synthesis Via Deep Reinforcement LearningCode0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with DemonstrationsCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Flexible Option LearningCode0
Free energy-based reinforcement learning using a quantum processorCode0
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion RecognitionCode0
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement LearningCode0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Causal State Distillation for Explainable Reinforcement LearningCode0
Financial Trading as a Game: A Deep Reinforcement Learning ApproachCode0
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary PredictionCode0
Causal Reasoning from Meta-reinforcement LearningCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
FeUdal Networks for Hierarchical Reinforcement LearningCode0
Deployable Reinforcement Learning with Variable Control RateCode0
Feudal Graph Reinforcement LearningCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement LearningCode0
Feature-Attending Recurrent Modules for Generalization in Reinforcement LearningCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Few-shot Quality-Diversity OptimizationCode0
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachCode0
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent SystemsCode0
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Benchmark Results

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