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 6526–6550 of 15113 papers

TitleStatusHype
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits—0
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning—0
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning—0
The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning—0
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation—0
The Optimal Reward Baseline for Gradient-Based Reinforcement Learning—0
The Option Keyboard: Combining Skills in Reinforcement Learning—0
Programmatic Reinforcement Learning: Navigating Gridworlds—0
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods—0
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer—0
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning—0
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation—0
Theoretical understanding of adversarial reinforcement learning via mean-field optimal control—0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning—0
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training—0
The Partially Observable History Process—0
The Phenomenon of Policy Churn—0
The Political Preferences of LLMs—0
The Power of Communication in a Distributed Multi-Agent System—0
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces—0
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning—0
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Benchmark Results

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