Chrome Dino Run using Reinforcement Learning
- URL: http://arxiv.org/abs/2008.06799v1
- Date: Sat, 15 Aug 2020 22:18:20 GMT
- Title: Chrome Dino Run using Reinforcement Learning
- Authors: Divyanshu Marwah, Sneha Srivastava, Anusha Gupta, Shruti Verma
- Abstract summary: We study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run.
We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reinforcement Learning is one of the most advanced set of algorithms known to
mankind which can compete in games and perform at par or even better than
humans. In this paper we study most popular model free reinforcement learning
algorithms along with convolutional neural network to train the agent for
playing the game of Chrome Dino Run. We have used two of the popular temporal
difference approaches namely Deep Q-Learning, and Expected SARSA and also
implemented Double DQN model to train the agent and finally compare the scores
with respect to the episodes and convergence of algorithms with respect to
timesteps.
Related papers
- RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement
Learning for Mario [0.0]
We implement the Reptile algorithm using the Super Mario Bros library and weights in Python, creating a neural network model.
We train the model using multiple tasks and episodes, choosing actions using the current neural network model, taking those actions in the environment, and updating the model using the Reptile algorithm.
Our results demonstrate that the Reptile algorithm provides a promising approach to few-shot learning in video game AI, with comparable or even better performance than the other two algorithms.
arXiv Detail & Related papers (2023-05-16T17:54:14Z) - Double A3C: Deep Reinforcement Learning on OpenAI Gym Games [0.0]
Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards.
We will propose and implement an improved version of Double A3C algorithm which utilizing the strength of both algorithms to play OpenAI Gym Atari 2600 games to beat its benchmarks.
arXiv Detail & Related papers (2023-03-04T00:06:27Z) - Mastering the Game of No-Press Diplomacy via Human-Regularized
Reinforcement Learning and Planning [95.78031053296513]
No-press Diplomacy is a complex strategy game involving both cooperation and competition.
We introduce a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy.
We show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL.
arXiv Detail & Related papers (2022-10-11T14:47:35Z) - Scalable Deep Reinforcement Learning Algorithms for Mean Field Games [60.550128966505625]
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents.
Recently, the question of learning equilibria in MFGs has gained momentum, particularly using model-free reinforcement learning (RL) methods.
Existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.
We propose two methods to address this shortcoming. The first one learns a mixed strategy from distillation of historical data into a neural network and is applied to the Fictitious Play algorithm.
The second one is an online mixing method based on
arXiv Detail & Related papers (2022-03-22T18:10:32Z) - Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise [8.938418994111716]
This article explores the possibility of replacing priors with noise and sample the noise from a Gaussian distribution to introduce more diversity into this algorithm.
We find that our modification of the Bootstrapped Deep Q-Learning algorithm achieves significantly higher evaluation scores across different types of Atari games.
arXiv Detail & Related papers (2022-03-02T10:28:14Z) - Retrieval-Augmented Reinforcement Learning [63.32076191982944]
We train a network to map a dataset of past experiences to optimal behavior.
The retrieval process is trained to retrieve information from the dataset that may be useful in the current context.
We show that retrieval-augmented R2D2 learns significantly faster than the baseline R2D2 agent and achieves higher scores.
arXiv Detail & Related papers (2022-02-17T02:44:05Z) - An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy
Image Compression Systems [73.48927855855219]
Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark.
In this paper, we perform the first large-scale comparison of recent state-of-the-art hybrid neural compression algorithms.
arXiv Detail & Related papers (2022-01-27T19:47:51Z) - Evolving Reinforcement Learning Algorithms [186.62294652057062]
We propose a method for meta-learning reinforcement learning algorithms.
The learned algorithms are domain-agnostic and can generalize to new environments not seen during training.
We highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games.
arXiv Detail & Related papers (2021-01-08T18:55:07Z) - Learning to Run with Potential-Based Reward Shaping and Demonstrations
from Video Data [70.540936204654]
"Learning to run" competition was to train a two-legged model of a humanoid body to run in a simulated race course with maximum speed.
All submissions took a tabula rasa approach to reinforcement learning (RL) and were able to produce relatively fast, but not optimal running behaviour.
We demonstrate how data from videos of human running can be used to shape the reward of the humanoid learning agent.
arXiv Detail & Related papers (2020-12-16T09:46:58Z) - Language Inference with Multi-head Automata through Reinforcement
Learning [0.0]
Six different languages are formulated as reinforcement learning problems.
Agents are modeled as simple multi-head automaton.
Genetic algorithm performs better than Q-learning algorithm in general.
arXiv Detail & Related papers (2020-10-20T09:11:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.