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

Showing 22012250 of 15113 papers

TitleStatusHype
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic ActorCode1
DeepMind Control SuiteCode1
Deep Reinforcement Learning for List-wise RecommendationsCode1
Whatever Does Not Kill Deep Reinforcement Learning, Makes It StrongerCode1
AI2-THOR: An Interactive 3D Environment for Visual AICode1
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning AlgorithmCode1
Time Limits in Reinforcement LearningCode1
Plan, Attend, Generate: Planning for Sequence-to-Sequence ModelsCode1
One-Shot Reinforcement Learning for Robot Navigation with Interactive ReplayCode1
Action Branching Architectures for Deep Reinforcement LearningCode1
Eigenoption Discovery through the Deep Successor RepresentationCode1
Learning Robust Rewards with Adversarial Inverse Reinforcement LearningCode1
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and DemonstrationsCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement LearningCode1
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximationCode1
Meta-SGD: Learning to Learn Quickly for Few-Shot LearningCode1
A Distributional Perspective on Reinforcement LearningCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Lenient Multi-Agent Deep Reinforcement LearningCode1
Emergence of Locomotion Behaviours in Rich EnvironmentsCode1
Hindsight Experience ReplayCode1
A Deep Reinforcement Learning Framework for the Financial Portfolio Management ProblemCode1
Value-Decomposition Networks For Cooperative Multi-Agent LearningCode1
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive EnvironmentsCode1
Thinking Fast and Slow with Deep Learning and Tree SearchCode1
ParlAI: A Dialog Research Software PlatformCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
Time-Contrastive Networks: Self-Supervised Learning from VideoCode1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
Robust Adversarial Reinforcement LearningCode1
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless NavigationCode1
Stabilising Experience Replay for Deep Multi-Agent Reinforcement LearningCode1
Multi-agent Reinforcement Learning in Sequential Social DilemmasCode1
An Alternative Softmax Operator for Reinforcement LearningCode1
Cryptocurrency Portfolio Management with Deep Reinforcement LearningCode1
Self-critical Sequence Training for Image CaptioningCode1
Neural Combinatorial Optimization with Reinforcement LearningCode1
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
Sample Efficient Actor-Critic with Experience ReplayCode1
Progressive Neural NetworksCode1
Generative Adversarial Imitation LearningCode1
OpenAI GymCode1
Deep Reinforcement Learning from Self-Play in Imperfect-Information GamesCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Investigating practical linear temporal difference learningCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
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Benchmark Results

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