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 19011910 of 15113 papers

TitleStatusHype
Robot Perception enables Complex Navigation Behavior via Self-Supervised LearningCode1
Model-based Adversarial Meta-Reinforcement LearningCode1
AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsCode1
Agent Modelling under Partial Observability for Deep Reinforcement LearningCode1
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous ControlCode1
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven ExplorationCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large GamesCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer SearchCode1
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Benchmark Results

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