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 6726–6750 of 15113 papers

TitleStatusHype
Imitating Past Successes can be Very Suboptimal—0
A Model-Based Reinforcement Learning Approach for PID Design—0
Introspective Experience Replay: Look Back When SurprisedCode0
Efficient entity-based reinforcement learning—0
Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review—0
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL—0
Goal-Space Planning with Subgoal Models—0
Effects of Safety State Augmentation on Safe Exploration—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Learning Generalized Wireless MAC Communication Protocols via Abstraction—0
Balancing Profit, Risk, and Sustainability for Portfolio Management—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation—0
Specification-Guided Learning of Nash Equilibria with High Social Welfare—0
Markovian Interference in Experiments—0
Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path—0
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning—0
Rapid Learning of Spatial Representations for Goal-Directed Navigation Based on a Novel Model of Hippocampal Place Fields—0
Learning Dynamics and Generalization in Reinforcement Learning—0
ARC -- Actor Residual Critic for Adversarial Imitation Learning—0
DDPG based on multi-scale strokes for financial time series trading strategy—0
Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning—0
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning—0
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Benchmark Results

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