In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human performance in backgammon, the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games. The purpose of this study is twofold. First, we propose two activation functions for neural network function approximation in reinforcement learning: the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU). The activation of the SiLU is computed by the sigmoid function multiplied by its input. Second, we suggest that the more traditional approach of using on-policy learning with eligibility traces, instead of experience replay, and softmax action selection can be competitive with DQN, without the need for a separate target network. We validate our proposed approach by, first, achieving new state-of-the-art results in both stochastic SZ-Tetris and Tetris with a small 10 × 10 board, using TD(λ) learning and shallow dSiLU network agents, and, then, by outperforming DQN in the Atari 2600 domain by using a deep Sarsa(λ) agent with SiLU and dSiLU hidden units.

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.

Modular Reinforcement Learning decomposes a monolithic task into several tasks with sub-goals and learns each one in parallel to solve the original problem. Such learning patterns can be traced in the brains of animals. Recent evidence in neuroscience shows that animals utilize separate systems for processing rewards and punishments, illuminating a different perspective for modularizing Reinforcement Learning tasks. MaxPain and its deep variant, Deep MaxPain, showed the advances of such dichotomy-based decomposing architecture over conventional Q-learning in terms of safety and learning efficiency. These two methods differ in policy derivation. MaxPain linearly unified the reward and punishment value functions and generated a joint policy based on unified values; Deep MaxPain tackled scaling problems in high-dimensional cases by linearly forming a joint policy from two sub-policies obtained from their value functions. However, the mixing weights in both methods were determined manually, causing inadequate use of the learned modules. In this work, we discuss the signal scaling of reward and punishment related to discounting factor γ, and propose a weak constraint for signaling design. To further exploit the learning models, we propose a state-value dependent weighting scheme that automatically tunes the mixing weights: hard-max and softmax based on a case analysis of Boltzmann distribution. We focus on maze-solving navigation tasks and investigate how two metrics (pain-avoiding and goal-reaching) influence each other's behaviors during learning. We propose a sensor fusion network structure that utilizes lidar and images captured by a monocular camera instead of lidar-only and image-only sensing. Our results, both in the simulation of three types of mazes with different complexities and a real robot experiment of an L-maze on Turtlebot3 Waffle Pi, showed the improvements of our methods.

The aim of the Cyber Rodent project is to understand the origins of our reward and affective systems by building artificial agents that share the same intrinsic constraints as natural agents: Self-preserva tion and self-reproduction. A Cyber Rodent is a robot that can search for and recharge from battery packs on the floor and copy its programs to a nearby agent through its infrared communication port. This article reviews our research topics so far, including (1) evolution of neural controllers, (2) learning of foraging and mating behaviors, (3) evolution of learning architectures and meta-parameters, (4) simultaneous learning of multiple agents in a body, and (5) learning and evolution in a self-sustained colony. We discuss our future directions and expected contributions.

This paper proposes model-free deep inverse reinforcement learning to find nonlinear reward function structures. We formulate inverse reinforcement learning as a problem of density ratio estimation, and show that the log of the ratio between an optimal state transition and a baseline one is given by a part of reward and the difference of the value functions under the framework of linearly solvable Markov decision processes. The logarithm of density ratio is efficiently calculated by binomial logistic regression, of which the classifier is constructed by the reward and state value function. The classifier tries to discriminate between samples drawn from the optimal state transition probability and those from the baseline one. Then, the estimated state value function is used to initialize the part of the deep neural networks for forward reinforcement learning. The proposed deep forward and inverse reinforcement learning is applied into two benchmark games: Atari 2600 and Reversi. Simulation results show that our method reaches the best performance substantially faster than the standard combination of forward and inverse reinforcement learning as well as behavior cloning.