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 22262250 of 15113 papers

TitleStatusHype
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