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 11101–11125 of 15113 papers

TitleStatusHype
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making—0
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos—0
A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy—0
Extrapolation in Gridworld Markov-Decision Processes—0
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems—0
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions—0
Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks—0
K-spin Hamiltonian for quantum-resolvable Markov decision processes—0
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder—0
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding—0
Kernel-Based Reinforcement Learning: A Finite-Time AnalysisCode0
Reinforcement Learning via Reasoning from Demonstration—0
Deep Reinforcement Learning for Process Control: A Primer for Beginners—0
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning—0
Self Punishment and Reward Backfill for Deep Q-LearningCode0
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels—0
Policy Gradient using Weak Derivatives for Reinforcement Learning—0
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion—0
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning—0
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
Adaptive Stress Testing without Domain Heuristics using Go-Explore—0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
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Benchmark Results

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