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 15101–15113 of 15113 papers

TitleStatusHype
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement LearningCode0
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics ModelsCode0
Constrained Reinforcement Learning for Dexterous ManipulationCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Reinforcement Learning from Hierarchical CriticsCode0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
Learning human behaviors from motion capture by adversarial imitationCode0
Action-depedent Control Variates for Policy Optimization via Stein's IdentityCode0
A Practical Guide to Multi-Objective Reinforcement Learning and PlanningCode0
Inherently Explainable Reinforcement Learning in Natural LanguageCode0
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic LearningCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
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Benchmark Results

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