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 4551–4560 of 15113 papers

TitleStatusHype
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents—0
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning—0
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving—0
A Tractable Inference Perspective of Offline RL—0
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
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Benchmark Results

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