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

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Showing 5776–5800 of 15113 papers

TitleStatusHype
Learning user-defined sub-goals using memory editing in reinforcement learning—0
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement LearningCode1
Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision ProcessesCode0
Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery—0
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning ApproachCode0
RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources—0
Toward Compositional Generalization in Object-Oriented World Modeling—0
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery—0
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning—0
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning StudyCode1
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems—0
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
Toward Policy Explanations for Multi-Agent Reinforcement LearningCode0
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learningCode0
Learning Eco-Driving Strategies at Signalized Intersections—0
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search—0
BATS: Best Action Trajectory Stitching—0
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition—0
Multi-objective Pointer Network for Combinatorial OptimizationCode0
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning MethodsCode0
Skill-based Meta-Reinforcement Learning—0
Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement LearningCode0
Task-Induced Representation Learning—0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
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Benchmark Results

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