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 13751–13800 of 15113 papers

TitleStatusHype
The Hierarchical Adaptive Forgetting Variational Filter—0
Do deep reinforcement learning agents model intentions?Code0
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning ApproachCode0
Low-pass Recurrent Neural Networks - A memory architecture for longer-term correlation discovery—0
GAN Q-learningCode0
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture—0
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization—0
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration—0
Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes—0
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis—0
Discourse-Aware Neural Rewards for Coherent Text Generation—0
Deep Reinforcement Learning for Optimal Control of Space Heating—0
End-to-End Reinforcement Learning for Automatic Taxonomy InductionCode0
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control—0
Reward Estimation for Variance Reduction in Deep Reinforcement LearningCode0
Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog—0
FFNet: Video Fast-Forwarding via Reinforcement LearningCode0
Deep Reinforcement Learning for Page-wise Recommendations—0
Multimodal Machine Translation with Reinforcement Learning—0
Planning and Learning with Stochastic Action Sets—0
Deep Reinforcement Learning for Playing 2.5D Fighting GamesCode0
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework—0
Exploration by Distributional Reinforcement Learning—0
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement LearningCode0
VINE: An Open Source Interactive Data Visualization Tool for NeuroevolutionCode0
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine TranslationCode0
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems—0
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and ReviewCode1
Robust Log-Optimal Strategy with Reinforcement Learning—0
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning—0
Dialog-based Interactive Image RetrievalCode0
Toward Diverse Text Generation with Inverse Reinforcement LearningCode1
Towards Experienced Anomaly Detector through Reinforcement Learning—0
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming—0
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence PredictionCode0
A Tree Search Algorithm for Sequence LabelingCode0
Sentiment Adaptive End-to-End Dialog Systems—0
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments—0
Decoupling Dynamics and Reward for Transfer LearningCode0
Action Categorization for Computationally Improved Task Learning and Planning—0
Multiagent Soft Q-Learning—0
Towards Symbolic Reinforcement Learning with Common SenseCode0
Distributed Distributional Deterministic Policy GradientsCode0
Benchmarking projective simulation in navigation problems—0
Crawling in Rogue's dungeons with (partitioned) A3CCode0
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server—0
Event Extraction with Generative Adversarial Imitation Learning—0
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World—0
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Benchmark Results

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