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 6676–6700 of 15113 papers

TitleStatusHype
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach—0
Intrinsically motivated option learning: a comparative study of recent methods—0
IGN : Implicit Generative NetworksCode0
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks—0
Provable Benefit of Multitask Representation Learning in Reinforcement Learning—0
Relative Policy-Transition Optimization for Fast Policy Transfer—0
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward—0
RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem—0
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning—0
Case-Based Inverse Reinforcement Learning Using Temporal CoherenceCode0
Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement—0
Federated Offline Reinforcement Learning—0
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
An application of neural networks to a problem in knot theory and group theory (untangling braids)—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement LearningCode0
Large-Scale Retrieval for Reinforcement Learning—0
Social Network Structure Shapes Innovation: Experience-sharing in RL with SAPIENS—0
Multifidelity Reinforcement Learning with Control Variates—0
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC—0
Regret Bounds for Information-Directed Reinforcement Learning—0
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information—0
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Benchmark Results

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