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inzva AI Projects #2 - Game Playing with Reinforcement Learning Project
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AugmentedRandomSearch
Cross Entropy Method
DiscreteSpace
EvolutionStrategy
ddpg
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README.md

README.md

game-playing-with-RL

inzva AI Projects #2 - Game Playing with Reinforcement Learning Project

Project-Description

This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms.

Project-Aim

We aim to learn RL algorithms and try to implement that to the RL games.

Descreate Space

  • Cartpole-v0 with VPG
  • Pong: The pendulum starts upright, and the goal is to prevent it from falling over.
  • The Taxi: In this lab, you will train a taxi to pick up and drop off passengers.

Continuous Space

  • MountainCarContinuous-v0 with Actor Critic/PPO
  • BipedalWalker-v2 with Deep Deterministic Policy Gradients (DDPG)/ Genetic Algorithms.

RL Algorithms

** Cross-Entropy Method

The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorialand continuous problems, with either a static or noisy objective. The method approximates the optimal importance sampling estimator by repeating two phases:

  • Draw a sample from a probability distribution.

  • Minimize the cross-entropy between this distribution and a target distribution to produce a better sample in the next iteration.

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