Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies

Abstract

Conventional reinforcement learning methods for Markov decision processes rely on weakly-guided, stochastic searches to drive the learning process. It can therefore be difficult to predict what agent behaviors might emerge. In this paper, we consider an information-theoretic cost function for performing constrained stochastic searches that promote the formation of risk-averse to risk-favoring behaviors. This cost function is the value of information, which provides the optimal trade-off between the expected return of a policy and the policy's complexity; policy complexity is measured by number of bits and controlled by a single hyperparameter on the cost function. As the policy complexity is reduced, the agents will increasingly eschew risky actions. This reduces the potential for high accrued rewards. As the policy complexity increases, the agents will take actions, regardless of the risk, that can raise the long-term rewards. The obtainable reward depends on a single, tunable hyperparameter that regulates the degree of policy complexity. We evaluate the performance of value-of-information-based policies on a stochastic version of Ms. Pac-Man. A major component of this paper is the demonstration that ranges of policy complexity values yield different game-play styles and explaining why this occurs. We also show that our reinforcement-learning search mechanism is more efficient than the others we utilize. This result implies that the value of information theory is appropriate for framing the exploitation-exploration trade-off in reinforcement learning.

IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games

References in corpus (3)

• Learning values across many orders of magnitude
• Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion
• An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits

Cited by in corpus (5)

• Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion
• Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
• An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits
• Reduction of Markov Chains using a Value-of-Information-Based Approach
• An Information-Theoretic Approach for Automatically Determining the Number of States when Aggregating Markov Chains