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 9451–9500 of 15113 papers

TitleStatusHype
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning—0
Model-Invariant State Abstractions for Model-Based Reinforcement Learning—0
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning—0
Sim-Env: Decoupling OpenAI Gym Environments from Simulation ModelsCode0
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners—0
Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming—0
Reinforcement Learning for Datacenter Congestion Control—0
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems—0
Strategic bidding in freight transport using deep reinforcement learning—0
Continuous Doubly Constrained Batch Reinforcement LearningCode0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs—0
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning—0
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method—0
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
Training Larger Networks for Deep Reinforcement Learning—0
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution—0
Transferring Domain Knowledge with an Adviser in Continuous Tasks—0
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments—0
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning—0
Improper Reinforcement Learning with Gradient-based Policy Optimization—0
Active Privacy-utility Trade-off Against a Hypothesis Testing Adversary—0
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Developing parsimonious ensembles using predictor diversity within a reinforcement learning frameworkCode0
How RL Agents Behave When Their Actions Are ModifiedCode0
Learning from Demonstrations using Signal Temporal Logic—0
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?Code0
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport—0
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement LearningCode0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Model-free Representation Learning and Exploration in Low-rank MDPs—0
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Reinforcement Learning for IoT Security: A Comprehensive Survey—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Domain Adversarial Reinforcement Learning—0
A Reinforcement learning method for Optical Thin-Film Design—0
Interactive Learning from Activity DescriptionCode0
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning—0
Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network—0
Modelling Cooperation in Network Games with Spatio-Temporal Complexity—0
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators—0
Q-Value Weighted Regression: Reinforcement Learning with Limited DataCode0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
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Benchmark Results

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