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 7476–7500 of 15113 papers

TitleStatusHype
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning—0
Maximum Entropy Dueling Network Architecture in Atari Domain—0
Maximum entropy GFlowNets with soft Q-learning—0
Maximum Entropy Hindsight Experience Replay—0
Adversarial Inverse Reinforcement Learning for Mean Field Games—0
Maximum Entropy Model-based Reinforcement Learning—0
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors—0
Maximum Entropy Reinforcement Learning with Mixture Policies—0
Maximum Entropy RL (Provably) Solves Some Robust RL Problems—0
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning—0
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees—0
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
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Benchmark Results

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