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 10326–10350 of 15113 papers

TitleStatusHype
Newton-type Methods for Minimax OptimizationCode0
A differential Hebbian framework for biologically-plausible motor control—0
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism—0
The NetHack Learning EnvironmentCode1
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement LearningCode0
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms—0
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain ClassifiersCode0
Control-Aware Representations for Model-based Reinforcement Learning—0
DISK: Learning local features with policy gradientCode1
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario—0
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration—0
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Experience Replay with Likelihood-free Importance WeightsCode1
Environment Shaping in Reinforcement Learning using State Abstraction—0
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control—0
On the Relationship Between Active Inference and Control as Inference—0
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning—0
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping—0
Show me the Way: Intrinsic Motivation from Demonstrations—0
Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty—0
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Benchmark Results

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