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Come Migliorare la Tua Esperienza con il Tennis M15 Bielsko Biala

1. Pianifica i tuoi giorni in base al calendario delle partite per non perdere nessun evento importante.
2. Mantieniti informato leggendo blog e articoli dedicati al tennis su piattaforme autorevoli.
3. Fai parte delle community online dove puoi scambiare opinioni ed esperienze con altri appassionati.
4. Educati sugli aspetti tecnici del gioco attraverso tutorial video e guide specializzate.
5. Investi nel tuo tempo libero seguendo allenamenti locali o partecipando a tornei amatoriali nella tua zona.

Analisi Approfondita delle Partite Chiave

Ogni settimana selezioniamo alcune partite da analizzare in dettaglio. Ecco cosa troverai nelle nostre analisi:

• Dati statistici sui servizi e risposte dei giocatori coinvolti.
• Sintesi delle prestazioni passate nei confronti reciproci tra i giocatori.
• Predizioni basate su modelli matematici avanzati che tengono conto delle variabili climatiche e psicologiche.
• Osservazioni sui punti chiave che hanno determinato l'esito dei match precedenti tra i due atleti.
• Suggerimenti su possibili mosse strategiche che potrebbero essere adottate durante il match in corso.

Eccellenti Interviste agli Esperti del Settore

Grazie alle nostre interviste esclusive con allenatori, giocatori ed esperti di scommesse, scoprirai gli insight più preziosi sul mondo del tennis M15 Bielsko Biala:

• Come si preparano mentalmente i giocatori prima delle grandi sfide?ckmoll/udacity_deep_reinforcement_learning<|file_sep|>/README.md # Deep Reinforcement Learning Nanodegree This repository contains my solutions to the projects of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) by Udacity. ## Projects 1. [Project 1: Navigation](./Navigation) 2. [Project 2: Continuous Control](./Continuous_Control) 3. [Project 3: Collaboration and Competition](./Collaboration_and_Competition) <|file_sep|># Collaboration and Competition This project is the third project of the Deep Reinforcement Learning Nanodegree by Udacity. ## Project details In this project you will work with the [Tennis](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Learning-Environment-Examples.md#tennis) environment.  The environment consists of two agents controlling rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01 (for each violation). Thus, the goal of each agent is to keep the ball in play. The observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping. The task is episodic, and in order to solve the environment, your agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both agents). Specifically, - After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 2 (potentially different) scores. We then take the maximum of these 2 scores. - This yields a single **score** for each episode. The environment is considered solved, when the average (over 100 episodes) of those **scores** is at least +0.5. ## Project description You will use an actor-critic method to train two agents that can collaborate to keep the ball in play. ### Environment The environment is based on Unity ML-Agents and can be downloaded from [here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P3/Tennis/Tennis.app.zip). Please follow the instructions to set up your Python environment with Unity ML-Agents before proceeding. ### Installation Clone this repository: git clone https://github.com/ckmoll/udacity_deep_reinforcement_learning.git cd udacity_deep_reinforcement_learning/Collaboration_and_Competition Install requirements: pip install -r requirements.txt ### Instructions Run training: python train.py Run trained agent: python train.py --play ### Files - [`train.py`](./train.py): script that trains an agent using actor-critic method and saves checkpoint - [`model.py`](./model.py): PyTorch model definition - [`checkpoint.pth`](./checkpoint.pth): checkpoint containing trained weights - [`report.pdf`](./report.pdf): report containing experiment results <|file_sep|># Project 1: Navigation This project is the first project of the Deep Reinforcement Learning Nanodegree by Udacity. ## Project details In this project you will learn how to solve an environment using Q-Learning with deep neural networks.  A double-jointed arm can move to target locations. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas. The state space has 37 dimensions and contains the arm's position, rotation, velocity, and angular velocities. The action space consists of four discrete actions: - `0` - move forward. - `1` - move backward. - `2` - turn left. - `3` - turn right. The task is episodic and you receive an **average score** over 100 episodes. ## Project description You will use Deep Q-Networks (DQN) to solve this problem. ### Environment The environment is based on Unity ML-Agents and can be downloaded from [here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux_NoVis.zip). Please follow the instructions to set up your Python environment with Unity ML-Agents before proceeding. ### Installation Clone this repository: git clone https://github.com/ckmoll/udacity_deep_reinforcement_learning.git cd udacity_deep_reinforcement_learning/Navigation Install requirements: pip install -r requirements.txt ### Instructions Run training: python train_dqn.py --play Run trained agent: python train_dqn.py --play --load_checkpoint checkpoint.pth.tar ### Files - [`train_dqn.py`](./train_dqn.py): script that trains an agent using DQN algorithm and saves checkpoint if necessary - [`dqn_agent.py`](./dqn_agent.py): DQN Agent definition - [`checkpoint.pth.tar`](./checkpoint.pth.tar): checkpoint containing trained weights (only present if `--save_checkpoint` argument was used during training) <|file_sep|># Project 2: Continuous Control This project is the second project of the Deep Reinforcement Learning Nanodegree by Udacity. ## Project details In this project you will learn how to solve an environment using policy gradient methods with deep neural networks.  In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible. The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of arm's joints. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1. The task is episodic and you receive an **average score** over 100 episodes. ## Project description You will use Policy Gradient methods based on actor-critic architecture (PPO algorithm) to solve this problem. ### Environment The environment is based on Unity ML-Agents and can be downloaded from [here](https://s3-us-west-1.amazonaws.com/udacity-drlnd/P2/Reacher/Reacher_Linux_NoVis.zip). Please follow the instructions to set up your Python environment with Unity ML-Agents before proceeding. ### Installation Clone this repository: git clone https://github.com/ckmoll/udacity_deep_reinforcement_learning.git cd udacity_deep_reinforcement_learning/Continuous_Control Install requirements: pip install -r requirements.txt ### Instructions Run training: python train.py --play Run trained agent: python train.py --play --load_checkpoint checkpoint.pth.tar ### Files - [`train.py`](./train.py): script that trains an agent using PPO algorithm and saves checkpoint if necessary - [`agent.py`](./agent.py): PPO Agent definition - [`checkpoint.pth.tar`](./checkpoint.pth.tar): checkpoint containing trained weights (only present if `--save_checkpoint` argument was used during training) <|repo_name|>ckmoll/udacity_deep_reinforcement_learning<|file_sep|>/Navigation/dqn_agent.py import numpy as np import random from collections import namedtuple from collections import deque