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Found 6 papers, 0 papers with code
Performance of NPG in Countable State-Space Average-Cost RL
We consider policy optimization methods in reinforcement learning settings where the state space is arbitrarily large, or even countably infinite.
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs).
On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
Since the exponential cost formulation deals with the multiplicative Bellman equation, our main contribution is a convergence proof which is quite different than existing results for discounted and risk-neutral average-cost problems as well as risk sensitive value and policy iteration approaches.
A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP
We study the risk-sensitive exponential cost MDP formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies.
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
Nonlinear Dynamics of Binocular Rivalry: A Comparative Study
When our eyes are presented with the same image, the brain processes it to view it as a single coherent one.
