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 10751–10775 of 15113 papers

TitleStatusHype
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning—0
The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling—0
The Importance of Credo in Multiagent Learning—0
The Importance of Sampling inMeta-Reinforcement Learning—0
The Ingredients of Real World Robotic Reinforcement Learning—0
The Ingredients of Real-World Robotic Reinforcement Learning—0
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study—0
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting—0
The Laplacian in RL: Learning Representations with Efficient Approximations—0
The Least Restriction for Offline Reinforcement Learning—0
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure—0
The Logical Options Framework—0
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
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Benchmark Results

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