Optimizing exploration parameter in dueling deep Q-networks for complex gaming environment

Reinforcement Learning is being used to solve various tasks. A Complex Environment is a recent problem at hand for Reinforcement Learning, which employs an Agent who interacts with the surroundings and learns to solve whatever task has to be done. To solve a Complex Environment efficiently using a Reinforcement Learning Agent, a lot of parameters are to be kept in perspective. Every action that the Agent takes has a consequence in the form of a Reward Function. Based on the value of this Reward Function, our Agent develops a Policy to solve the Environment. The Policy is generally developed to maximize the Reward Functions. The Optimal Policy employs an Exploration Strategy which is used by the Agent. Reinforcement Learning Architectures are relying on the Policy and Exploration Strategy of the Agent to solve the Environment efficiently. This research is based upon two parts. Firstly, the optimization of a Deep Reinforcement Learning Architecture “Dueling Deep Q-Network” is conducted by improving its Exploration strategy. It combines a recent and novel Exploration technique, Curiosity Driven Intrinsic Motivation, with the Dueling DQN. The performance of this Curious Dueling DQN is checked by comparing it with the existing Dueling DQN. Secondly, the performance of the Curious Dueling DQN is validated against Noisy Dueling DQN, a combination of Dueling DQN with another recent exploration strategy called Noisy Nets, hence, finding an optimal exploration strategy. The performance of both solutions is evaluated in the environment of Super Mario Bros based on Mean Score and Estimation Loss. The proposed model improves the Mean Score by 3 folds, while the loss is increased by 28%

Computationally Efficient Relational Reinforcement Learning

Relational Reinforcement Learning (RRL) is a technique that enables Reinforcement Learning (RL) agents to generalize from their experience, allowing them to learn over large or potentially infinite state spaces, to learn context sensitive behaviors, and to learn to solve variable goals and to transfer knowledge between similar situations. Prior RRL architectures are not sufficiently computationally efficient to see use outside of small, niche roles within larger Artificial Intelligence (AI) architectures. I present a novel online, incremental RRL architecture and an implementation that is orders of magnitude faster than its predecessors. The first aspect of this architecture that I explore is a computationally efficient implementation of an adaptive Hierarchical Tile Coding (aHTC), a kind of Adaptive Tile Coding (ATC) in which more general tiles which cover larger portions of the state-action space are kept as ones that cover smaller portions of the state-action space are introduced, using k-dimensional tries (k-d tries) to implement the value function for non-relational Temporal Difference (TD) methods. In order to achieve comparable performance for RRL, I implement the Rete algorithm to replace my k-d tries due to its efficient handling of both the variable binding problem and variable numbers of actions. Tying aHTCs and Rete together, I present a rule grammar that both maps aHTCs onto Rete and allows the architecture to automatically extract relational features in order to support adaptation of the value function over time. I experiment with several refinement criteria and additional functionality with which my agents attempt to determine if rerefinement using different features might allow them to better learn a near optimal policy. I present optimal results using a value criterion for several variants of BlocksWorld. I provide transfer results for BlocksWorld and a scalable Taxicab domain. I additionally introduce a Higher Order Grammar (HOG) that grants online, incremental RRL agents additional flexibility to introduce additional variables and corresponding relations as needed in order to learn effective value functions. I evaluate agents that use the HOG on a version of Blocks World and on an Adventure task. In summary, I present a new online, incremental RRL architecture, a grammar to map aHTCs onto the Rete, and an implementation that is orders of magnitude faster than its predecessors.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145859/1/bazald_1.pd

Relational Reinforcement Learning in Infinite Mario

Relational representations in reinforcement learning allow for the use of structural information like the presence of objects and relationships between them in the description of value functions. Through this paper, we show that such representations allow for the inclusion of background knowledge that qualitatively describes a state and can be used to design agents that demonstrate learning behavior in domains with large state and actions spaces such as computer games