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 3301–3310 of 15113 papers

TitleStatusHype
State Regularized Policy Optimization on Data with Dynamics Shift—0
Survival Instinct in Offline Reinforcement Learning—0
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous DrivingCode0
A General Perspective on Objectives of Reinforcement Learning—0
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control—0
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems—0
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-CriticCode1
For SALE: State-Action Representation Learning for Deep Reinforcement LearningCode1
Cycle Consistency Driven Object Discovery—0
Learning to Stabilize Online Reinforcement Learning in Unbounded State SpacesCode0
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Benchmark Results

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