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 9701–9750 of 15113 papers

TitleStatusHype
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Self-Supervised Continuous Control without Policy Gradient—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Re-examining Routing Networks for Multi-task Learning—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
PAC-Bayesian Randomized Value Function with Informative Prior—0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
Structure and randomness in planning and reinforcement learningCode0
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning—0
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks—0
RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS—0
When Is Generalizable Reinforcement Learning Tractable?—0
R-LAtte: Attention Module for Visual Control via Reinforcement Learning—0
What are the Statistical Limits of Batch RL with Linear Function Approximation?—0
Regioned Episodic Reinforcement Learning—0
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
Learning Latent Landmarks for Generalizable Planning—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
Learning Predictive Communication by Imagination in Networked System Control—0
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning—0
Incremental Policy Gradients for Online Reinforcement Learning Control—0
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents—0
Learning Safe Policies with Cost-sensitive Advantage Estimation—0
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation—0
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach—0
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Hindsight Curriculum Generation Based Multi-Goal Experience Replay—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Learning Efficient Planning-based Rewards for Imitation Learning—0
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Discrete Predictive Representation for Long-horizon Planning—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING—0
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Average Reward Reinforcement Learning with Monotonic Policy Improvement—0
Error Controlled Actor-Critic Method to Reinforcement Learning—0
Learning to Dynamically Select Between Reward Shaping Signals—0
Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents—0
Learning to Explore with Pleasure—0
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents—0
Learning to Observe with Reinforcement Learning—0
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Benchmark Results

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