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.
More closely related to our work are reinforcement learn-ing-based solutions to the task of conducting the ball (, @cite_22 ), which can be seen as a simplification of the dribbling task since it usually does not include adversaries.
@cite_22
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We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then transform that learned model into advice for a new task. A human teacher provides a mapping from the old task to the new task to guide this knowledge transfer. Advice is incorporated into our problem solver using a knowledge-based support vector regression method that we previously developed. This advice-taking approach allows the problem solver to refine or even discard the transferred knowledge based on its subsequent experiences. We empirically demonstrate the effectiveness of our approach with two games from the RoboCup soccer simulator: KeepAway and BreakAway. Our results demonstrate that a problem solver learning to play BreakAway using advice extracted from KeepAway outperforms a problem solver learning without the benefit of such advice. As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of generalizations: to previously unseen instructions and to longer sequences of instructions. For generalization over unseen instructions, we propose a new objective which encourages learning correspondences between similar subtasks by making analogies. For generalization over sequential instructions, we present a hierarchical architecture where a meta controller learns to use the acquired skills for executing the instructions. To deal with delayed reward, we propose a new neural architecture in the meta controller that learns when to update the subtask, which makes learning more efficient. Experimental results on a stochastic 3D domain show that the proposed ideas are crucial for generalization to longer instructions as well as unseen instructions. 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. Driven by recent developments in the area of Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The technology is termed Deep Reinforcement Learning (DRL) and combines the classic field of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. DRL enables solutions for difficult and high dimensional tasks, such as Atari game playing, for which previously proposed RL methods were inadequate. However, these new solution approaches still take a long time to learn how to actuate in such domains and so far are mainly researched for single task scenarios. The ability to generalize gathered knowledge and transfer it to another task has been researched for classical RL, but remains an open problem for the DRL domain. Consequently, in this article we evaluate under which conditions the application of Transfer Learning (TL) to the DRL domain improves the learning of a new task. Our results indicate that TL can greatly accelerate DRL when transferring knowledge from similar tasks, and that the similarity between tasks plays a key role in the success or failure of knowledge transfer.