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 5626–5650 of 15113 papers

TitleStatusHype
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search—0
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse—0
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning—0
IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture—0
IPM Move Planner: AN EFFICIENT EXPLOITING DEEP REINFORCEMENT LEARNING WITH MONTE CARLO TREE SEARCH—0
IPO: Interior-point Policy Optimization under Constraints—0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning—0
IR-Aware ECO Timing Optimization Using Reinforcement Learning—0
BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination—0
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning—0
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning—0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?—0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
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 Patients—0
Is Epicurus the father of Reinforcement Learning?—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Is High Variance Unavoidable in RL? A Case Study in Continuous Control—0
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 Learning—0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret—0
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Benchmark Results

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