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 8926–8950 of 15113 papers

TitleStatusHype
Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents.—0
Playing Catan with Cross-dimensional Neural Network—0
Playing Flappy Bird via Asynchronous Advantage Actor Critic Algorithm—0
Playing Go without Game Tree Search Using Convolutional Neural Networks—0
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments—0
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP—0
Playtesting: What is Beyond Personas—0
Play with Emotion: Affect-Driven Reinforcement Learning—0
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning—0
Plug and Play, Model-Based Reinforcement Learning—0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies—0
PODS: Policy Optimization via Differentiable Simulation—0
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation—0
Pointer Networks with Q-Learning for Combinatorial Optimization—0
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—0
Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone—0
Policy and Value Transfer in Lifelong Reinforcement Learning—0
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models—0
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
Policy-Based Trajectory Clustering in Offline Reinforcement Learning—0
Policy Certificates: Towards Accountable Reinforcement Learning—0
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
Policy Distillation and Value Matching in Multiagent Reinforcement Learning—0
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Benchmark Results

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