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 3451–3475 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Linear Quadratic Regulator Regions—0
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance—0
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Counterfactual Explanation Policies in RL—0
A Strong Baseline for Batch Imitation Learning—0
Auto-Encoding Adversarial Imitation Learning—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Auto-Encoding Inverse Reinforcement Learning—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
A Learning Framework for High Precision Industrial Assembly—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting—0
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Agent based modelling for continuously varying supply chains—0
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning—0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Show:102550
← PrevPage 139 of 605Next →

Benchmark Results

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