Deep Reinforcement Learning with Continuous and Discrete Value Functions

Deep Reinforcement Learning with Continuous and Discrete Value Functions – An initial stage of the reinforcement learning task requires an initial set of objectives, which must fit under the optimal state distribution. One approach is to use a single objective for each goal, which is very much preferable to other strategies in that it avoids over-fitting. Then a policy learning scheme is proposed to learn a policy, and a policy selection algorithm is proposed to explore the optimal policy for the task. The algorithm is based on the principle of selecting the optimum policy for the task, which leads to a single policy. Experimental results show that the policy selection algorithm performs better than other policy learning methods.

We present a multi-armed bandit-based game where players randomly choose actions that lead to them scoring the best actions, which are generated when the players play an action that can be used to increase the player’s score. In this paper, we extend the traditional multi-armed bandit game to allow players to use the game to make two choices at each round. This allows players to generate two actions at each round. We experimentally compare two variants of this game and show that the two variants are competitive and different in their performance. This suggests that, in terms of their ability to generate action proposals to maximize the reward, players are able to be more selective when making decisions in their immediate future.

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Deep Reinforcement Learning with Continuous and Discrete Value Functions

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    Predicting the outcome of long distance triathlons by augmentative learningWe present a multi-armed bandit-based game where players randomly choose actions that lead to them scoring the best actions, which are generated when the players play an action that can be used to increase the player’s score. In this paper, we extend the traditional multi-armed bandit game to allow players to use the game to make two choices at each round. This allows players to generate two actions at each round. We experimentally compare two variants of this game and show that the two variants are competitive and different in their performance. This suggests that, in terms of their ability to generate action proposals to maximize the reward, players are able to be more selective when making decisions in their immediate future.


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