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 15101–15113 of 15113 papers

TitleStatusHype
Structure Learning in Human Sequential Decision-Making—0
Optimization on a Budget: A Reinforcement Learning Approach—0
Near-optimal Regret Bounds for Reinforcement Learning—0
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation—0
Quantum reinforcement learningCode0
An Object-Oriented Representation for Efficient Reinforcement LearningCode0
Fitted Q-iteration in continuous action-space MDPs—0
Receding Horizon Differential Dynamic Programming—0
Online Linear Regression and Its Application to Model-Based Reinforcement Learning—0
Least-Squares Policy IterationCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
FROM DEEP LEARNING TO DEEP DEDUCING: AUTOMATICALLY TRACKING DOWN NASH EQUILIBRIUM THROUGH AUTONOMOUS NEURAL AGENT, A POSSIBLE MISSING STEP TOWARD GENERAL A.I.—0
Accidental exploration through value predictors—0
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Benchmark Results

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