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There have been success modeling card games with reinforcement learning (RL) using Deep Q-learning Network (DQN) with experience replay. Experience replay buffer is a large collection of tuples: (state (s), action (a), reward (r), next state (s′). An RL agent can learn which combinations lead to the highest reward. The representations are stored in a deep ...


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As I understand the question - a lot of this is about simple vision processing. If you already had a access to the position of the ball and the position of the two paddles, one has almost everything required to be able to play the game - the rest is machine learning. In the simplest sense the agent gets a sequence of tuples of 4 numbers, two for the ball and ...


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What is a great deception? It could be defined as a believable set of information aiming to a final deceitful objective. Just like any RL model, you can maximize a score thanks to small rewards leading to bad directions, and a great reward if the final reward is reached (ex: great loss of money). As a consequence, you have to make sure that steps could be ...


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There is definitely a lot of work to do on the NLP and knowledgebase side of things before you can realise your agent. However, as the question suggests, we can ignore those details and focus on: Can reinforcement learning (RL) be used to train a "deceptive" agent? The short answer is yes, this is entirely possible. In principle this is ...


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In a very general form, temporal difference (TD) learning is based on the idea that a value (typically a state value or action value) at time $t$ is related to the value at time $t+n$, and this can be used to improve estimates of the value at time $t$. In single-step TD learning using action values, the values that get related, and are used to drive the ...


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One example is "Self-improving Chatbots based on Reinforcement Learning" by Debmalya Biswas. There is a paper and code.


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