Transfer In Reinforcement Learning Research Articles

Learning Generalizable and Composable Abstractions for Transfer in Reinforcement Learning

Reinforcement Learning (RL) in complex environments presents many challenges: agents require learning concise representations of both environments and behaviors for efficient reasoning and generalizing experiences to new, unseen situations. However, RL approaches can be sample-inefficient and difficult to scale, especially in long-horizon sparse reward settings. To address these issues, the goal of my doctoral research is to develop methods that automatically construct semantically meaningful state and temporal abstractions for efficient transfer and generalization. In my work, I develop hierarchical approaches for learning transferable, generalizable knowledge in the form of symbolically represented options, as well as for integrating search techniques with RL to solve new problems by efficiently composing the learned options. Empirical results show that the resulting approaches effectively learn and transfer knowledge, achieving superior sample efficiency compared to SOTA methods while also enhancing interpretability.

Investigating the properties of neural network representations in reinforcement learning

In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning focused on designing fixed-basis architectures to achieve properties thought to be desirable, such as orthogonality and sparsity. In contrast, the idea behind deep reinforcement learning methods is that the agent designer should not encode representational properties, but rather that the data stream should determine the properties of the representation—good representations emerge under appropriate training schemes. In this paper we bring these two perspectives together, empirically investigating the properties of representations that support transfer in reinforcement learning. We introduce and measure six representational properties over more than 25,000 agent-task settings. We consider Deep Q-learning agents with different auxiliary losses in a pixel-based navigation environment, with source and transfer tasks corresponding to different goal locations. We develop a method to better understand why some representations work better for transfer, through a systematic approach varying task similarity and measuring and correlating representation properties with transfer performance. We demonstrate the generality of the methodology by investigating representations learned by a Rainbow agent that successfully transfers across Atari 2600 game modes.

Using digital twin to enhance Sim2real transfer for reinforcement learning in 3C assembly

PurposeThis paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.Design/methodology/approachThis work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.FindingsIn this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.Originality/valueIn this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model’s perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.

Hypernetworks for Zero-Shot Transfer in Reinforcement Learning

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.

Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation

Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation

A taxonomy for similarity metrics between Markov decision processes

Although the notion of task similarity is potentially interesting in a wide range of areas such as curriculum learning or automated planning, it has mostly been tied to transfer learning. Transfer is based on the idea of reusing the knowledge acquired in the learning of a set of source tasks to a new learning process in a target task, assuming that the target and source tasks are close enough. In recent years, transfer learning has succeeded in making reinforcement learning (RL) algorithms more efficient (e.g., by reducing the number of samples needed to achieve (near-)optimal performance). Transfer in RL is based on the core concept of similarity: whenever the tasks are similar, the transferred knowledge can be reused to solve the target task and significantly improve the learning performance. Therefore, the selection of good metrics to measure these similarities is a critical aspect when building transfer RL algorithms, especially when this knowledge is transferred from simulation to the real world. In the literature, there are many metrics to measure the similarity between MDPs, hence, many definitions of similarity or its complement distance have been considered. In this paper, we propose a categorization of these metrics and analyze the definitions of similarity proposed so far, taking into account such categorization. We also follow this taxonomy to survey the existing literature, as well as suggesting future directions for the construction of new metrics.

Visual transfer for reinforcement learning via gradient penalty based Wasserstein domain confusion

Visual transfer for reinforcement learning via gradient penalty based Wasserstein domain confusion

Visual Transfer For Reinforcement Learning Via Wasserstein Domain Confusion

We introduce Wasserstein Adversarial Proximal Policy Optimization (WAPPO), a novel algorithm for visual transfer in Reinforcement Learning that explicitly learns to align the distributions of extracted features between a source and target task. WAPPO approximates and minimizes the Wasserstein-1 distance between the distributions of features from source and target domains via a novel Wasserstein Confusion objective. WAPPO outperforms the prior state-of-the-art in visual transfer and successfully transfers policies across Visual Cartpole and both the easy and hard settings of of 16 OpenAI Procgen environments.

強化学習における知識の転移 : 異なるタスクへの適応( 知識の転移)

強化学習における知識の転移 : 異なるタスクへの適応( 知識の転移)

DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning

Having the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-based Reasoning (CBR) there exists a general methodology that provides a framework for knowledge transfer which has been underrepresented in the RL literature so far. We formulate a terminology for the CBR framework targeted towards RL researchers with the goal of facilitating communication between the respective research communities. Based on this framework, we propose the Deep Case-based Policy Inference (DECAF) algorithm to accelerate learning by building a library of cases and reusing them if they are similar to a new task when training a new policy. DECAF guides the training by dynamically selecting and blending policies according to their usefulness for the current target task, reusing previously learned policies for a more effective exploration but still enabling the adaptation to particularities of the new task. We show an empirical evaluation in the Atari game playing domain depicting the benefits of our algorithm with regards to sample efficiency, robustness against negative transfer, and performance increase when compared to state-of-the-art methods.

Latent Structure Matching for Knowledge Transfer in Reinforcement Learning

Reinforcement learning algorithms usually require a large number of empirical samples and give rise to a slow convergence in practical applications. One solution is to introduce transfer learning: Knowledge from well-learned source tasks can be reused to reduce sample request and accelerate the learning of target tasks. However, if an unmatched source task is selected, it will slow down or even disrupt the learning procedure. Therefore, it is very important for knowledge transfer to select appropriate source tasks that have a high degree of matching with target tasks. In this paper, a novel task matching algorithm is proposed to derive the latent structures of value functions of tasks, and align the structures for similarity estimation. Through the latent structure matching, the highly-matched source tasks are selected effectively, from which knowledge is then transferred to give action advice, and improve exploration strategies of the target tasks. Experiments are conducted on the simulated navigation environment and the mountain car environment. The results illustrate the significant performance gain of the improved exploration strategy, compared with traditional ϵ -greedy exploration strategy. A theoretical proof is also given to verify the improvement of the exploration strategy based on latent structure matching.

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactlyrelevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.

Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.

The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks. For example, in robot navigation, environment-centric information may include the robot's geographic location, while agent-centric information may include sensor readings of various nearby obstacles. We propose that these situations provide an opportunity for a very natural style of knowledge transfer, in which the agent learns to predict actions' environmental consequences using agent-centric information. These predictions contain important information about the affordances and dangers present in a novel environment, and can effectively transfer knowledge from agent-centric to environment-centric learning systems. Using several example problems including spatial navigation and network routing, we show that our knowledge transfer approach can allow faster and lower cost learning than existing alternatives.

Reinforcement learning transfer based on subgoal discovery and subtask similarity

This paper studies the problem of transfer learning in the context of reinforcement learning. We propose a novel transfer learning method that can speed up reinforcement learning with the aid of previously learnt tasks. Before performing extensive learning episodes, our method attempts to analyze the learning task via some exploration in the environment, and then attempts to reuse previous learning experience whenever it is possible and appropriate. In particular, our proposed method consists of four stages: 1) subgoal discovery, 2) option construction, 3) similarity searching, and 4) option reusing. Especially, in order to fulfill the task of identifying similar options, we propose a novel similarity measure between options, which is built upon the intuition that similar options have similar state-action probabilities. We examine our algorithm using extensive experiments, comparing it with existing methods. The results show that our method outperforms conventional non-transfer reinforcement learning algorithms, as well as existing transfer learning methods, by a wide margin.

Context transfer in reinforcement learning using action-value functions.

This paper discusses the notion of context transfer in reinforcement learning tasks. Context transfer, as defined in this paper, implies knowledge transfer between source and target tasks that share the same environment dynamics and reward function but have different states or action spaces. In other words, the agents learn the same task while using different sensors and actuators. This requires the existence of an underlying common Markov decision process (MDP) to which all the agents' MDPs can be mapped. This is formulated in terms of the notion of MDP homomorphism. The learning framework is Q-learning. To transfer the knowledge between these tasks, the feature space is used as a translator and is expressed as a partial mapping between the state-action spaces of different tasks. The Q-values learned during the learning process of the source tasks are mapped to the sets of Q-values for the target task. These transferred Q-values are merged together and used to initialize the learning process of the target task. An interval-based approach is used to represent and merge the knowledge of the source tasks. Empirical results show that the transferred initialization can be beneficial to the learning process of the target task.

Transfer in reinforcement learning via shared features

We present a framework for transfer in reinforcement learning based on the idea that related tasks share some common features, and that transfer can be achieved via those shared features. The framework attempts to capture the notion of tasks that are related but distinct, and provides some insight into when transfer can be usefully applied to a problem sequence and when it cannot. We apply the framework to the knowledge transfer problem, and show that an agent can learn a portable shaping function from experience in a sequence of tasks to significantly improve performance in a later related task, even given a very brief training period. We also apply the framework to skill transfer, to show that agents can learn portable skills across a sequence of tasks that significantly improve performance on later related tasks, approaching the performance of agents given perfectly learned problem-specific skills.

Structural knowledge transfer by spatial abstraction for reinforcement learning agents

In this article we investigate the role of abstraction principles for knowledge transfer in agent control learning tasks. We analyze abstraction from a formal point of view and characterize three distinct facets: aspectualization, coarsening, and conceptual classification. The taxonomy we develop allows us to interrelate existing approaches to abstraction, leading to a code of practice for designing knowledge representations that support knowledge transfer. We detail how aspectualization can be utilized to achieve knowledge transfer in reinforcement learning. We propose the use of so-called structure space aspectualizable knowledge representations that explicate structural properties of the state space and present a posteriori structure space aspectualization (APSST) as a method to extract generally sensible behavior from a learned policy. This new policy can be used for knowledge transfer to support learning new tasks in different environments. Finally, we present a case study that demonstrates transfer of generally sensible navigation skills from simple simulation to a real-world robotic platform.