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 3176–3200 of 15113 papers

TitleStatusHype
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations—0
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement LearningCode0
Generative Job Recommendations with Large Language Model—0
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning—0
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management—0
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Model-Bellman Inconsistency for Model-based Offline Reinforcement LearningCode1
Decentralized Motor Skill Learning for Complex Robotic Systems—0
Navigation of micro-robot swarms for targeted delivery using reinforcement learning—0
Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules—0
ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch—0
Probabilistic Constraint for Safety-Critical Reinforcement Learning—0
Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning—0
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand CoresCode1
Laxity-Aware Scalable Reinforcement Learning for HVAC Control—0
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning—0
MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Structure in Deep Reinforcement Learning: A Survey and Open Problems—0
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes—0
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning—0
Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments—0
Automatic Truss Design with Reinforcement LearningCode1
Machine-learning based noise characterization and correction on neutral atoms NISQ devices—0
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Benchmark Results

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