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 9501–9525 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Backup Strategies against Adversaries—0
Discovery of Options via Meta-Learned Subgoals—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Representation Matters: Offline Pretraining for Sequential Decision Making—0
Risk-Averse Bayes-Adaptive Reinforcement Learning—0
Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Patterns, predictions, and actions: A story about machine learning—0
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning—0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Personalization for Web-based Services using Offline Reinforcement Learning—0
Derivative-Free Reinforcement Learning: A Review—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms—0
Learning Equational Theorem Proving—0
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users—0
Learning State Representations from Random Deep Action-conditional PredictionsCode0
Adaptive Pairwise Weights for Temporal Credit Assignment—0
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning—0
Measuring Progress in Deep Reinforcement Learning Sample Efficiency—0
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature—0
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Benchmark Results

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