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 5401–5425 of 15113 papers

TitleStatusHype
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
Defending Observation Attacks in Deep Reinforcement Learning via Detection and DenoisingCode0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Transformers are Meta-Reinforcement LearnersCode1
RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture SearchCode1
Universally Expressive Communication in Multi-Agent Reinforcement LearningCode0
Solving the capacitated vehicle routing problem with timing windows using rollouts and MAX-SAT—0
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization—0
Open-Ended Learning Strategies for Learning Complex Locomotion Skills—0
Visual Radial Basis Q-Network—0
Robust Reinforcement Learning with Distributional Risk-averse formulation—0
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning—0
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward—0
Reinforcement Learning-based Placement of Charging Stations in Urban Road NetworksCode1
Provable Benefit of Multitask Representation Learning in Reinforcement Learning—0
IGN : Implicit Generative NetworksCode0
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach—0
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks—0
Intrinsically motivated option learning: a comparative study of recent methods—0
Relative Policy-Transition Optimization for Fast Policy Transfer—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
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
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Benchmark Results

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