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 7726–7750 of 15113 papers

TitleStatusHype
Distilled Domain Randomization—0
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
Deep Policy Iteration with Integer Programming for Inventory Management—0
Reinforcement learning for options on target volatility funds—0
An Analytical Update Rule for General Policy Optimization—0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Differentially Private Exploration in Reinforcement Learning with Linear Representation—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
Convergence Guarantees for Deep Epsilon Greedy Policy Learning—0
Architecting and Visualizing Deep Reinforcement Learning Models—0
Towards Personalization of User Preferences in Partially Observable Smart Home Environments—0
Maximum Entropy Model-based Reinforcement Learning—0
A Generic Graph Sparsification Framework using Deep Reinforcement LearningCode0
Towards Interactive Reinforcement Learning with Intrinsic Feedback—0
Reward-Free Attacks in Multi-Agent Reinforcement Learning—0
Sample Complexity of Robust Reinforcement Learning with a Generative ModelCode0
Safe Reinforcement Learning for Grid Voltage Control—0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks—0
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems—0
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision ProcessesCode0
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement LearningCode0
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning—0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms—0
Structural Credit Assignment in Neural Networks using Reinforcement Learning—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
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Benchmark Results

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