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 5676–5700 of 15113 papers

TitleStatusHype
Market Making via Reinforcement Learning in China Commodity Market—0
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL—0
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement LearningCode0
Multibit Tries Packet Classification with Deep Reinforcement Learning—0
Moral reinforcement learning using actual causation—0
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space—0
DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and GazeboCode0
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement LearningCode1
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning—0
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning—0
Attacking and Defending Deep Reinforcement Learning Policies—0
Deep Apprenticeship Learning for Playing Games—0
A Deep Reinforcement Learning Blind AI in DareFightingICE—0
Reachability Constrained Reinforcement LearningCode1
Rethinking Reinforcement Learning based Logic Synthesis—0
Towards on-sky adaptive optics control using reinforcement learning—0
Taming Continuous Posteriors for Latent Variational Dialogue Policies—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
q-Munchausen Reinforcement Learning—0
The Primacy Bias in Deep Reinforcement LearningCode1
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents—0
RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states—0
Policy Gradient Method For Robust Reinforcement Learning—0
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning—0
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Benchmark Results

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