Reinforcement Learning In Continuous Spaces Research Articles

Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance Weighting.

In this paper, a systematic incremental learning method is presented for reinforcement learning in continuous spaces where the learning environment is dynamic. The goal is to adjust the previously learned policy in the original environment to a new one incrementally whenever the environment changes. To improve the adaptability to the ever-changing environment, we propose a two-step solution incorporated with the incremental learning procedure: policy relaxation and importance weighting. First, the behavior policy is relaxed to a random one in the initial learning episodes to encourage a proper exploration in the new environment. It alleviates the conflict between the new information and the existing knowledge for a better adaptation in the long term. Second, it is observed that episodes receiving higher returns are more in line with the new environment, and hence contain more new information. During parameter updating, we assign higher importance weights to the learning episodes that contain more new information, thus encouraging the previous optimal policy to be faster adapted to a new one that fits in the new environment. Empirical studies on continuous controlling tasks with varying configurations verify that the proposed method achieves a significantly faster adaptation to various dynamic environments than the baselines.

Policy derivation methods for critic-only reinforcement learning in continuous spaces

This paper addresses the problem of deriving a policy from the value function in the context of critic-only reinforcement learning (RL) in continuous state and action spaces. With continuous-valued states, RL algorithms have to rely on a numerical approximator to represent the value function. Numerical approximation due to its nature virtually always exhibits artifacts which damage the overall performance of the controlled system. In addition, when continuous-valued action is used, the most common approach is to discretize the action space and exhaustively search for the action that maximizes the right-hand side of the Bellman equation. Such a policy derivation procedure is computationally involved and results in steady-state error due to the lack of continuity. In this work, we propose policy derivation methods which alleviate the above problems by means of action space refinement, continuous approximation, and post-processing of the V-function by using symbolic regression. The proposed methods are tested on nonlinear control problems: 1-DOF and 2-DOF pendulum swing-up problems, and on magnetic manipulation. The results show significantly improved performance in terms of cumulative return and computational complexity.

Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous Actions

Despite their proven effectiveness, many Michigan learning classifier systems (LCSs) cannot perform multistep reinforcement learning in continuous spaces. To meet this technical challenge, some LCSs have been designed to learn fuzzy logic rules. They can be largely classified into strength-based and accuracy-based systems. The latter is gaining more research attention in the last decade. However, existing accuracy-based learning systems either address primarily single-step learning problems or require the action space to be discrete. In this paper, a new accuracy-based learning fuzzy classifier system (LFCS) is developed to explicitly handle continuous state input and continuous action output during multistep reinforcement learning. Several technical improvements have been achieved while developing the new learning algorithm. Particularly, we have successfully extended ${Q}$ -learning like credit assignment methods to continuous spaces. To enable direct learning of stochastic strategies for action selection, we have also proposed to use a new fuzzy logic system with stochastic action outputs. Moreover, fine-grained learning of fuzzy rules has been achieved effectively in our algorithm by using a natural gradient learning method. It is the first time that these techniques are utilized substantially in any accuracy-based LFCSs. Meanwhile, in comparison with several recently proposed learning algorithms, our algorithm is shown to perform highly competitively on four benchmark learning problems and a robotics problem. The practical usefulness of our algorithm is also demonstrated by improving the performance of a wireless body area network.

Cooperative Behavior Acquisition of Multi-Robot Systems Based on Reinforcement Learning in Continuous Space

Cooperative Behavior Acquisition of Multi-Robot Systems Based on Reinforcement Learning in Continuous Space

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

2P1-3F-A7 連続空間における強化学習を用いたマルチロボットシステムの協調行動獲得

2P1-3F-A7 連続空間における強化学習を用いたマルチロボットシステムの協調行動獲得