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 12551–12600 of 15113 papers

TitleStatusHype
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP—0
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Probabilistic hypergraph grammars for efficient molecular optimization—0
Reinforcement Learning When All Actions are Not Always AvailableCode0
Measurement-based Online Available Bandwidth Estimation employing Reinforcement Learning—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
Autonomous Reinforcement Learning of Multiple Interrelated Tasks—0
Off-Policy Evaluation via Off-Policy Classification—0
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning—0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
Options as responses: Grounding behavioural hierarchies in multi-agent RL—0
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves—0
Robust exploration in linear quadratic reinforcement learningCode0
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies—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
Proximal Reliability Optimization for Reinforcement Learning—0
Adversarial Exploitation of Policy Imitation—0
Learning to solve the credit assignment problemCode0
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach—0
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications—0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites—0
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement LearningCode0
On the Correctness and Sample Complexity of Inverse Reinforcement LearningCode0
Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual NavigationCode0
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
An Empirical Study on Hyperparameters and their Interdependence for RL Generalization—0
The Principle of Unchanged Optimality in Reinforcement Learning Generalization—0
Enhanced Bayesian Compression via Deep Reinforcement Learning—0
Exploiting Noisy Data in Distant Supervision Relation Classification—0
Language-Driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model—0
Harnessing Reinforcement Learning for Neural Motion PlanningCode0
Decision-Making in Reinforcement Learning—0
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic TasksCode0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
Interval timing in deep reinforcement learning agentsCode0
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement LearningCode0
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning—0
Reinforcement Learning Experience Reuse with Policy Residual Representation—0
Towards Finding Longer ProofsCode0
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
Reinforcement Learning for Mean Field Game—0
On Value Functions and the Agent-Environment Boundary—0
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
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Benchmark Results

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