Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Partially Observable Markov Decision Processes (POMOPs) constitute an important class of reinforcement learning problems which present unique theoretical and computational difficulties. In the absence of the Markov property, popular reinforcement learning algorithms such as Q-Iearning may no longer be effective, and memory-based methods which remove partial observability via state-estimation are notoriously expensive. An alternative approach is to seek a stochastic memoryless policy which for each observation of the environment prescribes a probability distribution over available actions that maximizes the average reward per timestep. A reinforcement learning algorithm which learns a locally optimal stochastic memoryless policy has been proposed by Jaakkola, Singh and Jordan, but not empirically verified.  present a variation of this algorithm, discuss its implementation, and demonstrate its viability using four test problems.
['John K. Williams', 'Satinder P. Singh']
I am writing a research paper on a idea Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes
[{"from": "human", "value": "I am writing a research paper on a idea Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Partially Observable Markov Decision Processes (POMOPs) constitute an important class of reinforcement learning problems which present unique theoretical and computational difficulties. In the absence of the Markov property, popular reinforcement learning algorithms such as Q-Iearning may no longer be effective, and memory-based methods which remove partial observability via state-estimation are notoriously expensive. An alternative approach is to seek a stochastic memoryless policy which for each observation of the environment prescribes a probability distribution over available actions that maximizes the average reward per timestep. A reinforcement learning algorithm which learns a locally optimal stochastic memoryless policy has been proposed by Jaakkola, Singh and Jordan, but not empirically verified.  present a variation of this algorithm, discuss its implementation, and demonstrate its viability using four test problems."}]