Imagination-Augmented Agents for Deep Reinforcement Learning

Introduction

An interesting research area in Reinforcement Learning is developing AI for playing video games. Before Deep Learning, the AI for video games is coded based on Monte-Carlo Tree Search of pre-set rules. In recent researches, Deep Reinforcement Learning shown the success in playing video games like Atari 2600 games. To be specific, the method is called Deep Q-Learning(DQN) which learns the optimal actions based on current observations(raw pixels). However, there are some complex games where DQN fails to learn: some games need to solve a sub-problem without explicit reward or contain irreversible domains, where actions can be catastrophic. A typical example of these games is Sokoban(Wikipedia). Even as humans are playing the game, planning and inference are needed. This kind of game raises challenges to RL.

In Reinforcement Learning, the algorithms can be divided into two categories: model-free algorithm and model-based algorithm. DQN, mentioned above, is a model-free method. It takes raw pixels as input and maps them to values or actions. As a drawback, large amounts of training data is required. In addition, the policies are not generalized to new tasks in the same environment. A model-based method is trying to build a model for the environment. By querying the model, agents can avoid irreversible, poor decisions. As an approximation of the environment, it can enable better generalization across states. However, this method only shows success in limited settings, where an exact transition model is given or in simple domains. In complex environments, model-based methods suffer from model errors from function approximation. Currently, there is no model-based method that is robust against imperfections.

In this paper, the authors introduce a novel deep reinforcement learning architecture called Imagination-Augmented Agents(I2As). Literally, this method enables agents to learn to interpret predictions from a learned environment model to construct implicit plans. It is a combination of model-free and model-based aspects. The advantage of this method is that it learns in an end-to-end way to extract information from model simulations without making any assumptions about the structure or the perfections of the environment model. As shown in the results, this method outperforms DQN in the games: Sokoban, and MniniPacman. In addition, the experiments all show that I2A is able to successfully use imperfect models.

Motivation

Although the structure of this method is complex, the motivation is intuitive: since the agent suffers from irreversible decisions, attempts in simulated stated may be helpful. To improve the expensive search space in traditional MCTS methods, adding decision from policy network can reduce search steps. In order to keep context information, rollout results are encoded by an LSTM encoder. The final output is combining the result from model-free network and model-based network.

Related Work

There are some works that try to apply deep learning to model-based reinforcement learning. The popular approach is to learn a neural network from the environment and apply the network in classical planning algorithms. These works can not handle the mismatch between the learned model and the ground truth. Liu et al.(2017) use context information from trajectories, but in terms of imitation learning.

To deal with imperfect models, Deisenroth and Rasmussen(2011) try to capture model uncertainty by applying high-computational Gaussian Process models.

Similar ideas can be found in a study by Hamrick et al.(2017): they present a neural network that queries expert models, but just focus on meta-control for continuous contextual bandit problems. Pascanu et al.(2017) extend this work by focusing on explicit planning in sequential environments.

Approach

The architecture of I2A can be seen in Figure 2. The observation $O_t$ is fed into two paths, the model-free path is just common DQN which predicts the best action given $O_t$, whereas the model-based path performs a rollout strategy and aggregate all encoded rollout results. Together they are used to generate a policy function $\pi$ to output an action.