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

Showing 56015650 of 15113 papers

TitleStatusHype
Inverse Reinforcement Learning with Locally Consistent Reward Functions0
Inverse Reinforcement Learning with Missing Data0
Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home0
Inverse Reinforcement Learning with Multiple Ranked Experts0
Inverse Reinforcement Learning with Natural Language Goals0
Inverse Risk-Sensitive Reinforcement Learning0
Investigating Enactive Learning for Autonomous Intelligent Agents0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain0
Investigating Generalisation in Continuous Deep Reinforcement Learning0
Investigating Recurrence and Eligibility Traces in Deep Q-Networks0
Investigating Reinforcement Learning Agents for Continuous State Space Environments0
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations0
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning0
Investigating the Edge of Stability Phenomenon in Reinforcement Learning0
Investigating the Impact of Action Representations in Policy Gradient Algorithms0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls0
Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems0
Investigating the Properties of Neural Network Representations in Reinforcement Learning0
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions0
Investigating Vulnerabilities of Deep Neural Policies0
Investigation of Factorized Optical Flows as Mid-Level Representations0
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments0
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems0
Investigation of reinforcement learning for shape optimization of profile extrusion dies0
Investigation of Sentiment Controllable Chatbot0
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search0
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse0
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning0
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture0
IPM Move Planner: AN EFFICIENT EXPLOITING DEEP REINFORCEMENT LEARNING WITH MONTE CARLO TREE SEARCH0
IPO: Interior-point Policy Optimization under Constraints0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning0
IR-Aware ECO Timing Optimization Using Reinforcement Learning0
BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination0
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning0
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies0
Is Conditional Generative Modeling all you need for Decision-Making?0
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients0
Is Epicurus the father of Reinforcement Learning?0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning0
Is High Variance Unavoidable in RL? A Case Study in Continuous Control0
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?0
Is Long Horizon RL More Difficult Than Short Horizon RL?0
A Survey and Critique of Multiagent Deep Reinforcement Learning0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning0
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret0
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Benchmark Results

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