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 10751–10800 of 15113 papers

TitleStatusHype
Control-Aware Representations for Model-based Reinforcement Learning—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration—0
Environment Shaping in Reinforcement Learning using State Abstraction—0
Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty—0
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping—0
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control—0
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning—0
Show me the Way: Intrinsic Motivation from Demonstrations—0
On the Relationship Between Active Inference and Control as Inference—0
Near-Optimal Reinforcement Learning with Self-Play—0
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data—0
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving—0
dm_control: Software and Tasks for Continuous Control—0
Ecological Reinforcement Learning—0
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Automated Optical Multi-layer Design via Deep Reinforcement LearningCode0
Reinforcement Learning for Mean Field Games with Strategic Complementarities—0
Gradient-EM Bayesian Meta-learning—0
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees—0
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies—0
Entropic Risk Constrained Soft-Robust Policy Optimization—0
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms—0
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet—0
A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines—0
NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration—0
FISAR: Forward Invariant Safe Reinforcement Learning with a Deep Neural Network-Based Optimize—0
On Reward-Free Reinforcement Learning with Linear Function Approximation—0
Provably adaptive reinforcement learning in metric spaces—0
WD3: Taming the Estimation Bias in Deep Reinforcement Learning—0
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs—0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning—0
Deep Reinforcement Learning amidst Lifelong Non-Stationarity—0
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles—0
Delta Schema Network in Model-based Reinforcement LearningCode0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Introduction to Machine Learning for Accelerator Physics—0
Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and ControlCode0
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
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Benchmark Results

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