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

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Showing 1721–1730 of 15113 papers

TitleStatusHype
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsCode1
On Reinforcement Learning for the Game of 2048Code1
On Simple Reactive Neural Networks for Behaviour-Based Reinforcement LearningCode1
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement LearningCode1
On the Importance of Hyperparameter Optimization for Model-based Reinforcement LearningCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
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Benchmark Results

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