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 5651–5700 of 15113 papers

TitleStatusHype
On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach—0
Learning Progress Driven Multi-Agent CurriculumCode0
Adversarial joint attacks on legged robots—0
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning—0
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence DistributionsCode0
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-CommunicationCode1
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental ChangesCode0
Routing and Placement of Macros using Deep Reinforcement Learning—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble—0
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise—0
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment RegimesCode0
Data Valuation for Offline Reinforcement Learning—0
AIGenC: An AI generalisation model via creativity—0
IL-flOw: Imitation Learning from Observation using Normalizing Flows—0
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems—0
Generating Explanations from Deep Reinforcement Learning Using Episodic Memory—0
A2C is a special case of PPOCode1
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control TasksCode0
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning—0
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability—0
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
Show:102550
← PrevPage 114 of 303Next →

Benchmark Results

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