Deep RL based solution of LunarLander-v2 environment.
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Updated
Apr 4, 2019 - Jupyter Notebook
Deep RL based solution of LunarLander-v2 environment.
Useless try to create neural network
Deep Q-Network example from Udacity's Deep Reinforcement Learning Nanodegree.
Self-solving control problems from OpenAI Gym with NEAT
We apply DQN algorithm to make and artificial agent learn how to land space-craft on moon.
Behaviour Cloning On OpenAI Environment
This is a project of reinforcement learning which contains two different environments. The first environment is the taxi driver problem in 4x4 space with the simple Q-learning update rule. In this task, we compared the performance of the e-greedy policy and Boltzmann policy. As a second environment, we chose the LunarLander from the open gym. Fo…
Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.
Deep learning and Neural Networks course labs&homeworks&assignments
Deep Q-Learning algorithms to solve LunarLander-v2.
Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2
Deep Reinforcement Learning on Lunar Lander gym environment
RL with OpenAI Gym
Teaching to an agent to play the Lunar Lander game from OpenAI Gym using REINFORCE.
OpenAI LunarLander-v2 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
Semester project for the AI Applications class of the MSc in Artificial Intelligence
Experiment 1: Comparison of key bandit algorithms; Experiment 2: Comparison of Q and SARSA Learning on Taxiv3 environment' ; Experiment 3: Comparison of Q, SARSA and CEM Learning on LunarLanderv2 Environment
Implementation of reinforcement learning algorithms for the OpenAI Gym environment LunarLander-v2
Implement RL algorithms in PyTorch and test on Gym environments.
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