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 2131–2140 of 15113 papers

TitleStatusHype
Model-based reinforcement learning for protein backbone design—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots—0
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based ModellingCode1
Learning Optimal Deterministic Policies with Stochastic Policy Gradients—0
Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk—0
Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient ManipulationCode5
Tabular and Deep Reinforcement Learning for Gittins Index—0
Constrained Reinforcement Learning Under Model Mismatch—0
Reinforcement Learning-Guided Semi-Supervised Learning—0
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Benchmark Results

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