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 9901–9925 of 15113 papers

TitleStatusHype
Semi-Supervised Off Policy Reinforcement Learning—0
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems—0
Resolving Implicit Coordination in Multi-Agent Deep Reinforcement Learning with Deep Q-Networks & Game TheoryCode0
Emergence of Different Modes of Tool Use in a Reaching and Dragging Task—0
Efficient Reservoir Management through Deep Reinforcement Learning—0
Battery Model Calibration with Deep Reinforcement Learning—0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation—0
Model-Agnostic Learning to Meta-Learn—0
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments—0
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment DesignCode0
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach—0
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation—0
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks—0
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points—0
Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching—0
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER—0
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Benchmark Results

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