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 3001–3025 of 15113 papers

TitleStatusHype
Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Asynchronous Coagent Networks—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
Asymptotics of Language Model Alignment—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
A Guiding Principle for Causal Decision Problems—0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning—0
Asymptotic Bias of Stochastic Gradient Search—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
A Guider Network for Multi-Dual Learning—0
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
Asymmetric Actor Critic for Image-Based Robot Learning—0
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model—0
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning—0
Deep Hedging of Derivatives Using Reinforcement Learning—0
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Benchmark Results

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