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 4526–4550 of 15113 papers

TitleStatusHype
Out-of-Distribution-Aware Electric Vehicle Charging—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
LLM Augmented Hierarchical Agents—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Stable Modular Control via Contraction Theory for Reinforcement Learning—0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Low-Rank MDPs with Continuous Action Spaces—0
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
High-dimensional Bid Learning for Energy Storage Bidding in Energy Markets—0
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models—0
Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario—0
Domain Randomization via Entropy Maximization—0
Imitation Bootstrapped Reinforcement Learning—0
Towards model-free RL algorithms that scale well with unstructured data—0
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula—0
Using General Value Functions to Learn Domain-Backed Inventory Management Policies—0
Learning Realistic Traffic Agents in Closed-loop—0
Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability—0
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning—0
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning—0
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards—0
Rethinking Decision Transformer via Hierarchical Reinforcement Learning—0
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity—0
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Benchmark Results

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