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 21762200 of 15113 papers

TitleStatusHype
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement LearningCode1
Learning a Decentralized Multi-arm Motion PlannerCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Blockchain Framework for Artificial Intelligence ComputationCode1
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
Contrastive Active InferenceCode1
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Large Neighborhood Search based on Neural Construction HeuristicsCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Contextualized Rewriting for Text SummarizationCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Constructions in combinatorics via neural networksCode1
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic ManipulationCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Large Language Model as a Policy Teacher for Training Reinforcement Learning AgentsCode1
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Benchmark Results

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