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 5651–5675 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
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Benchmark Results

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