GratifAI

Compared various agents like Sarsa(0),Expected Sarsa and Q-Learning on the ‘Windy Grid world’ problem.
Experimented with LSTM-A3C model to develop an AI for playing Breakout (arcade game) and trying to beat it
Developed an AI, a Deep Convolutional Q-Learning model combined to Eligibility Traces for playing ’Doom’ which is a first person shooting game and trying to beat the high score

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
Breakout		Breakout
Doom_AI		Doom_AI
LICENSE		LICENSE
README.md		README.md
approx_control.py		approx_control.py
approx_prediction.py		approx_prediction.py
bayesian_bandit.py		bayesian_bandit.py
bayesian_normal.py		bayesian_normal.py
bayesian_starter.py		bayesian_starter.py
cartpole.py		cartpole.py
comparing_epsilons.py		comparing_epsilons.py
comparing_explore_exploit_methods.py		comparing_explore_exploit_methods.py
epsilon_greedy.py		epsilon_greedy.py
epsilon_greedy_starter.py		epsilon_greedy_starter.py
monte_carlo.py		monte_carlo.py
monte_carlo_es.py		monte_carlo_es.py
monte_carlo_no_es.py		monte_carlo_no_es.py
optimistic.py		optimistic.py
optimistic_initial_values.py		optimistic_initial_values.py
optimistic_starter.py		optimistic_starter.py
plot_rl_rewards.py		plot_rl_rewards.py
q_learning.py		q_learning.py
sarsa.py		sarsa.py
td0_prediction.py		td0_prediction.py
tic_tac_toe.py		tic_tac_toe.py
ucb1.py		ucb1.py
ucb1_starter.py		ucb1_starter.py

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