Bellman equation
PulseAugur coverage of Bellman equation — every cluster mentioning Bellman equation across labs, papers, and developer communities, ranked by signal.
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New framework tackles offline maximum entropy reinforcement learning
Researchers have developed a new framework for offline maximum entropy reinforcement learning that separates environment modeling from value function learning. This approach utilizes conditional diffusion models based o…
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New DFL method for MDPs tackles scalability with occupancy measure approach
Researchers have developed a new approach to decision-focused learning (DFL) for Markov decision processes (MDPs) that addresses scalability limitations of existing methods. By reformulating the MDP as an occupancy meas…
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New framework uses diffusion models for offline reinforcement learning
Researchers have developed a new framework for offline reinforcement learning that uses diffusion models to estimate the optimal action-value function, Q*. This method addresses the challenge of unknown reward functions…
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New arXiv papers explore risk-based MDPs and Bellman equation dualities
Two new research papers published on arXiv explore advanced concepts in sequential decision-making for artificial intelligence. The first paper introduces ERQDP, a novel method for finite-horizon Markov decision process…
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New theory bridges Newton-Raphson method and Regularized Policy Iteration
Researchers have established a formal equivalence between the Newton-Raphson method and Regularized Policy Iteration (RPI) when applied to regularized Markov Decision Processes (RMDPs). This connection, particularly evi…
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New CEFOL algorithm uses deep learning for complex dynamic programming problems
Researchers have developed a new deep learning algorithm called CEFOL (Certainty-Equivalent First-Order Learning) designed to tackle complex discrete-time dynamic programming problems with recursive utility. This algori…
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Google DeepMind: RL agents may implicitly model environments
Researchers at Google DeepMind have demonstrated a method to recover an agent's world model by inverting the Bellman equation, which is typically used to determine optimal policies. This work suggests that reinforcement…