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 7951–7975 of 15113 papers

TitleStatusHype
Many Agent Reinforcement Learning Under Partial Observability—0
Modelling resource allocation in uncertain system environment through deep reinforcement learning—0
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual PoliciesCode1
Learning from Demonstration without DemonstrationsCode0
CROP: Certifying Robust Policies for Reinforcement Learning through Functional SmoothingCode0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks—0
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL—0
Automatic Curricula via Expert Demonstrations—0
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Safe Reinforcement Learning Using Advantage-Based InterventionCode1
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles—0
Unbiased Methods for Multi-Goal Reinforcement Learning—0
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine TranslationCode1
Mungojerrie: Reinforcement Learning of Linear-Time Objectives—0
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image TranslationCode1
Solving Continuous Control with Episodic MemoryCode1
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and DefensesCode0
Reinforcement Learning for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?—0
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number—0
Offline RL Without Off-Policy EvaluationCode1
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot SoccerCode1
Robust Reinforcement Learning Under Minimax Regret for Green SecurityCode0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning—0
Deep reinforcement learning on a multi-asset environment for trading—0
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Benchmark Results

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