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 8201–8225 of 15113 papers

TitleStatusHype
Non-Cooperative Inverse Reinforcement Learning—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Non Deterministic Logic Programs—0
Non-Deterministic Policies in Markovian Decision Processes—0
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning—0
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling—0
Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation—0
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration—0
Non-Markovian policies occupancy measures—0
Non-Markovian Reinforcement Learning using Fractional Dynamics—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions—0
Nonparametric Bayesian Policy Priors for Reinforcement Learning—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Nonparametric General Reinforcement Learning—0
Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration—0
Non-Robust Feature Mapping in Deep Reinforcement Learning—0
Non-stationary Reinforcement Learning under General Function Approximation—0
Nonstationary Reinforcement Learning with Linear Function Approximation—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design—0
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning—0
No-Press Diplomacy: Modeling Multi-Agent Gameplay—0
No-Regret Exploration in Goal-Oriented Reinforcement Learning—0
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Benchmark Results

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