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 12401–12450 of 15113 papers

TitleStatusHype
DeepMDP: Learning Continuous Latent Space Models for Representation Learning—0
Deep Reinforcement Learning for Multi-objective Optimization—0
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem—0
Measurement-based Online Available Bandwidth Estimation employing Reinforcement Learning—0
Reinforcement Learning When All Actions are Not Always AvailableCode0
Probabilistic hypergraph grammars for efficient molecular optimization—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm—0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning—0
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves—0
Options as responses: Grounding behavioural hierarchies in multi-agent RL—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
Autonomous Reinforcement Learning of Multiple Interrelated Tasks—0
Robust exploration in linear quadratic reinforcement learningCode0
Off-Policy Evaluation via Off-Policy Classification—0
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning—0
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies—0
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies—0
Proximal Reliability Optimization for Reinforcement Learning—0
Adversarial Exploitation of Policy Imitation—0
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites—0
Learning to solve the credit assignment problemCode0
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach—0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement LearningCode0
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications—0
The Principle of Unchanged Optimality in Reinforcement Learning Generalization—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints—0
An Empirical Study on Hyperparameters and their Interdependence for RL Generalization—0
On the Correctness and Sample Complexity of Inverse Reinforcement LearningCode0
Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual NavigationCode0
Exploiting Noisy Data in Distant Supervision Relation Classification—0
Decision-Making in Reinforcement Learning—0
Harnessing Reinforcement Learning for Neural Motion PlanningCode0
Enhanced Bayesian Compression via Deep Reinforcement Learning—0
Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model—0
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic TasksCode0
Interval timing in deep reinforcement learning agentsCode0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
Reinforcement Learning Experience Reuse with Policy Residual Representation—0
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement LearningCode0
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning—0
On Value Functions and the Agent-Environment Boundary—0
Reinforcement Learning for Mean Field Game—0
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework—0
Combating the Compounding-Error Problem with a Multi-step Model—0
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Benchmark Results

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