Massively Parallel Methods for Deep Reinforcement Learning (original) (raw)

Playing Atari with Deep Reinforcement Learning

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.

Applying Q ( λ )-learning in Deep Reinforcement Learning to Play Atari Games

2017

In order to accelerate the learning process in high dimensional reinforcement learning problems, TD methods such as Q-learning and Sarsa are usually combined with eligibility traces. The recently introduced DQN (Deep Q-Network) algorithm, which is a combination of Q-learning with a deep neural network, has achieved good performance on several games in the Atari 2600 domain. However, the DQN training is very slow and requires too many time steps to converge. In this paper, we use the eligibility traces mechanism and propose the deep Q(λ) network algorithm. The proposed method provides faster learning in comparison with the DQN method. Empirical results on a range of games show that the deep Q(λ) network significantly reduces learning time.

Evolution of Reinforcement Learning: From Q-Learning to Deep

International Journal of Research in Engineering and Applied Sciences(IJREAS), 2021

Reinforcement Learning (RL) has emerged as a pivotal area in artificial intelligence, revolutionizing the way agents learn optimal behaviors through interaction with their environment. This paper explores the evolution of RL techniques, tracing the journey from traditional Q-learning to the advent of Deep Q-Networks (DQN). Initially, Q-learning provided a foundational framework for value-based learning, enabling agents to make decisions by estimating action-value functions. However, the limitations of Q-learning in handling high-dimensional state spaces necessitated the integration of deep learning techniques. The introduction of DQNs marked a significant breakthrough, leveraging deep neural networks to approximate Q-values, which allowed for the successful application of RL in complex environments such as video games and robotic control. This review discusses the advancements in algorithmic strategies, architectural designs, and the resulting impact on various applications. Furthermore, we highlight current trends and future directions in reinforcement learning research, emphasizing the ongoing quest to create more efficient and robust learning algorithms.

Comparison of Deep Reinforcement Learning Approaches for Intelligent Game Playing

2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), 2019

In Reinforcement Learning, a category of machine learning, learning is based on evaluative feedbacks without any supervised signals. The paper presents work aimed to understand the deep reinforcement learning approaches to creating such intelligent agents, by reproducing existing research and comparing their results. The project uses the Atari 2600 game called Breakout, in which the agent will learn control policies using deep reinforcement learning approaches to achieve a high score. The project explores two deep reinforcement learning approaches, Asynchronous Advantage actor-critic and Deep Q-Learning, both proposed by the DeepMind team, to train intelligent agents that can interact with an environment with automatic feature engineering thus requiring minimal domain knowledge.

Asynchronous Deep Q-Learning for Breakout with RAM inputs

2016

We implemented Asynchronous Deep Q-learning to learn the Atari 2600 game Breakout with RAM inputs. We tested the performance of the our agent by varying network structure, training policy, and environment settings. We saw the he most notable improvement through changing the environment settings. Furthermore, we observed interesting training effects when we used a Boltzmann-Q Policy that encouraged exploration by putting an upper bound on the greediness of the algorithm.

State of the Art Control of Atari Games Using Shallow Reinforcement Learning

Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2016

The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning. Its promise was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general competency in AI. It achieved dramatically better results than earlier approaches, showing that its ability to learn good representations is quite robust and general. This paper attempts to understand the principles that underlie DQN's impressive performance and to better contextualize its success. We systematically evaluate the importance of key representational biases encoded by DQN's network by proposing simple linear representations that make use of these concepts. Incorporating these characteristics, we obtain a computationally practical feature set that achieves competitive performance to DQN in the ALE. Besides offering insight into the strengths and weaknesses of DQN, we provide a generic representation for the ALE, significantly reducing the burden of learning a representation for each game. Moreover, we also provide a simple, reproducible benchmark for the sake of comparison to future work in the ALE.

Deep reinforcement learning compared with Q-table learning applied to backgammon

2016

Reinforcement learning attempts to mimic how humans react to their surrounding environment by giving feedback to software agents based on the actions they take. To test the capabilities of these agents, researches have long regarded board games as a powerful tool. This thesis compares two approaches to reinforcement learning in the board game backgammon, a Q-table and a deep reinforcement network. It was determined which approach surpassed the other in terms of accuracy and convergence rate towards the perceived optimal strategy. The evaluation is performed by training the agents using the self-learning approach. After variable amounts of training sessions, the agents are benchmarked against each other and a third, random agent. The results derived from the study indicate that the convergence rate of the deep learning agent is far superior to that of the Q-table agent. However, the results also indicate that the accuracy of Q-tables is greater than that of deep learning once the for...

DEEP REINFORCEMENT LEARNING: AN OVERVIEW

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We start with background of deep learning and reinforcement learning, as well as introduction of testbeds. Next we discuss Deep Q-Network (DQN) and its extensions, asynchronous methods, policy optimization, reward, and planning. After that, we talk about attention and memory, unsupervised learning, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, spoken dialogue systems (a.k.a. chatbot), machine translation, text sequence prediction, neural architecture design, personalized web services, healthcare, finance, and music generation. We mention topics/papers not reviewed yet. After listing a collection of RL resources, we close with discussions. 2 We discuss how/why we organize the overview from Section 3 to Section 21 in the current way: starting with RL fundamentals: value function/control, policy, reward, and planning (model in to-do list); next attention and memory, unsupervised learning, and learning to learn, which, together with transfer/semi-supervised/oneshot learning, etc, would be critical mechanisms for RL; then various applications.

DEEP REINFORCEMENT LEARNING: A SURVEY

IAEME PUBLICATION, 2020

Reinforcement learning (RL) is poised to revolutionize the sector of AI, and represents a step toward building autonomous systems with a higher-level understanding of the real world. Currently, Deep Learning (DL) is enabling reinforcement learning (RL) to scale to issues that were previously intractable, like learning to play video games directly from pixels. Deep Reinforcement Learning (DRL) algorithms are applied to AI, allowing control policies for robots to be learned directly from camera inputs within the world. The success of Reinforcement Learning (RL) is because of its strong mathematical roots within the principles of deep learning, Monte Carlo simulation, function approximation, and Artificial Intelligence (AI). Topics treated in some details during this survey are: Temporal variations, Q-Learning, semi-MDPs and stochastic games. Many recent advances in Deep Reinforcement Learning (DRL), eg. Policy gradients and hierarchical Reinforcement Learning (RL), are covered besides references. Pointers to various examples of applications are provided. Since no presently available technique works in all situations, this paper tends to propose guidelines for using previous information regarding the characteristics of the control problem at hand to decide on the suitable experience replay strategy.

Wide and Deep Reinforcement Learning for Grid-based Action Games

Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019

For the last decade Deep Reinforcement Learning has undergone exponential development; however, less has been done to integrate linear methods into it. Our Wide and Deep Reinforcement Learning framework provides a tool that combines linear and non-linear methods into one. For practical implementations, our framework can help integrate expert knowledge while improving the performance of existing Deep Reinforcement Learning algorithms. Our research aims to generate a simple practical framework to extend such algorithms. To test this framework we develop an extension of the popular Deep Q-Networks algorithm, which we name Wide Deep Q-Networks. We analyze its performance compared to Deep Q-Networks and Linear Agents, as well as human players. We apply our new algorithm to Berkley's Pac-Man environment. Our algorithm considerably outperforms Deep Q-Networks' both in terms of learning speed and ultimate performance showing its potential for boosting existing algorithms.