We propose a reinforcement learning solution to the soccer dribbling task, a scenario in which a soccer agent has to go from the beginning to the end of a region keeping possession of the ball, as an adversary attempts to gain possession. While the adversary uses a stationary policy, the dribbler learns the best action to take at each decision point. After defining meaningful variables to represent the state space, and high-level macro-actions to incorporate domain knowledge, we describe our application of the reinforcement learning algorithm Sarsa with CMAC for function approximation. Our experiments show that, after the training period, the dribbler is able to accomplish its task against a strong adversary around 58 of the time.
Reinforcement learning has long been applied to the robot soccer domain. For example, Andou @cite_15 uses observational reinforcement learning" to refine a function that is used by the soccer agents for deciding their positions on the field. Riedmiller @cite_10 use reinforcement learning to learn low-level soccer skills, such as kicking and ball-interception. Nakashima @cite_14 propose a reinforcement learning meth-od called fuzzy Q-learning", where an agent determines its action based on the inference result of a fuzzy rule-based system. The authors apply the proposed method to the sce-nario where a soccer agent learns to intercept a passed ball.
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In this paper, we propose a reinforcement learning method called a fuzzy Q-learning where an agent determines its action based on the inference result by a fuzzy rule-based system. We apply the proposed method to a soccer agent that intercepts a passed ball by another agent. In the proposed method, the state space is represented by internal information the learning agent maintains such as the relative velocity and the relative position of the ball to the learning agent. We divide the state space into several fuzzy subspaces. A fuzzy if-then rule in the proposed method represents a fuzzy subspace in the state space. The consequent part of the fuzzy if-then rules is a motion vector that suggests the moving direction and velocity of the learning agent. A reward is given to the learning agent if the distance between the ball and the agent becomes smaller or if the agent catches up with the ball. It is expected that the learning agent finally obtains the efficient positioning skill. Reinforcement learning is a type of unsupervised learning for sequential decision making. Q-learning is probably the best-understood reinforcement learning algorithm. In Q-learning, the agent learns a mapping from states and actions to their utilities. An important assumption of Q-learning is the Markovian environment assumption, meaning that any information needed to determine the optimal actions is reflected in the agent''s state representation. Consider an agent whose state representation is based solely on its immediate perceptual sensations. When its sensors are not able to make essential distinctions among world states, the Markov assumption is violated, causing a problem called perceptual aliasing. For example, when facing a closed box, an agent based on its current visual sensation cannot act optimally if the optimal action depends on the contents of the box. There are two basic approaches to addressing this problem -- using more sensors or using history to figure out the current world state. This paper studies three connectionist approaches which learn to use history to handle perceptual aliasing: the window-Q, recurrent-Q, and recurrent-model architectures. Empirical study of these architectures is presented. Their relative strengths and weaknesses are also discussed. Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel framework, Fine Grained Action Repetition (FiGAR), which enables the agent to decide the action as well as the time scale of repeating it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm which maintains an explicit policy estimate by enabling temporal abstractions in the action space. We empirically demonstrate the efficacy of our framework by showing performance improvements on top of three policy search algorithms in different domains: Asynchronous Advantage Actor Critic in the Atari 2600 domain, Trust Region Policy Optimization in Mujoco domain and Deep Deterministic Policy Gradients in the TORCS car racing domain. This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC.