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 6551–6575 of 15113 papers

TitleStatusHype
Distributional Decision Transformer for Hindsight Information Matching—0
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning—0
Distributionally Adaptive Meta Reinforcement Learning—0
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport—0
Distributionally Robust Imitation Learning—0
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity—0
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation—0
Distributionally Robust Reinforcement Learning—0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
Distributional Method for Risk Averse Reinforcement Learning—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Distributional reinforcement learning with linear function approximation—0
Distributional Reinforcement Learning with Ensembles—0
Distributional Reinforcement Learning with Monotonic Splines—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Distributional Reward Decomposition for Reinforcement Learning—0
Distributional Robustness and Regularization in Reinforcement Learning—0
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
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Benchmark Results

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