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 9551–9575 of 15113 papers

TitleStatusHype
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems—0
MBMF: Model-Based Priors for Model-Free Reinforcement Learning—0
Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods—0
A parallel-network continuous quantitative trading model with GARCH and PPO—0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
Option Transfer and SMDP Abstraction with Successor Features—0
MDPFuzz: Testing Models Solving Markov Decision Processes—0
MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments—0
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)—0
Mean Field Games Flock! The Reinforcement Learning Way—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach—0
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning—0
Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study—0
Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning—0
Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer—0
Measurement-based adaptation protocol with quantum reinforcement learning—0
Measurement-based Online Available Bandwidth Estimation employing Reinforcement Learning—0
Measurement Optimization under Uncertainty using Deep Reinforcement Learning—0
Measuring and Characterizing Generalization in Deep Reinforcement Learning—0
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning—0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—0
Measuring Progress in Deep Reinforcement Learning Sample Efficiency—0
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark—0
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Benchmark Results

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