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 3251–3275 of 15113 papers

TitleStatusHype
Human-Level Control without Server-Grade HardwareCode0
Concurrent Meta Reinforcement LearningCode0
Hierarchical Text Generation and Planning for Strategic DialogueCode0
Concurrent Credit Assignment for Data-efficient Reinforcement LearningCode0
Concrete DropoutCode0
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary RewardsCode0
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative ModelCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Hierarchical Object Detection with Deep Reinforcement LearningCode0
Hierarchical Meta Reinforcement Learning for Multi-Task EnvironmentsCode0
Hierarchically Structured Task-Agnostic Continual LearningCode0
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable PoliciesCode0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
Reinforcement Learning from Hierarchical CriticsCode0
Data Valuation using Reinforcement LearningCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Computing the Feedback Capacity of Finite State Channels using Reinforcement LearningCode0
DCUR: Data Curriculum for Teaching via Samples with Reinforcement LearningCode0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
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Benchmark Results

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