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 14401–14450 of 15113 papers

TitleStatusHype
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming—0
UCB Exploration via Q-Ensembles—0
Towards Synthesizing Complex Programs from Input-Output Examples—0
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics—0
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning—0
Reinforcement Learning for Learning Rate Control—0
The Atari Grand Challenge DatasetCode0
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget—0
Universal Reinforcement Learning Algorithms: Survey and ExperimentsCode0
Constrained Policy OptimizationCode0
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning—0
Experience Replay Using Transition Sequences—0
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation ModelsCode0
End-to-end Active Object Tracking via Reinforcement Learning—0
Free energy-based reinforcement learning using a quantum processorCode0
Latent Intention Dialogue ModelsCode0
Role Playing Learning for Socially Concomitant Mobile Robot Navigation—0
Boltzmann Exploration Done Right—0
Cross-Domain Perceptual Reward Functions—0
First-spike based visual categorization using reward-modulated STDP—0
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning—0
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach—0
Enhanced Experience Replay Generation for Efficient Reinforcement Learning—0
Visual Semantic Planning using Deep Successor Representations—0
Reinforcement Learning with a Corrupted Reward ChannelCode0
Safe Model-based Reinforcement Learning with Stability GuaranteesCode0
Thinking Fast and Slow with Deep Learning and Tree SearchCode1
A unified view of entropy-regularized Markov decision processes—0
Concrete DropoutCode0
AIXIjs: A Software Demo for General Reinforcement LearningCode0
Guide Actor-Critic for Continuous ControlCode0
Ask the Right Questions: Active Question Reformulation with Reinforcement LearningCode0
Experience enrichment based task independent reward model—0
Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning—0
Shallow Updates for Deep Reinforcement Learning—0
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning—0
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences—0
Atari games and Intel processors—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Delving into adversarial attacks on deep policies—0
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement LearningCode0
ParlAI: A Dialog Research Software PlatformCode1
Automatic Goal Generation for Reinforcement Learning AgentsCode0
New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -—0
Emotion in Reinforcement Learning Agents and Robots: A Survey—0
Repeated Inverse Reinforcement Learning—0
Efficient Parallel Methods for Deep Reinforcement LearningCode0
A Deep Reinforced Model for Abstractive SummarizationCode1
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
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Benchmark Results

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