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For questions related to reinforcement learning, i.e. a machine learning technique where we imagine an agent that interacts with an environment (composed of states) in time steps by taking actions and receiving rewards (or reinforcements), then, based on these interactions, the agent tries to find a policy (i.e. a behavioural strategy) that maximizes the cumulative reward (in the long run), so the goal of the agent is to maximize the reward.

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I am playing with a deep Q-learning algorithm in my own environment. The network can perform well as long as there is only one enemy. My agent can perform the following actions: do_nothing prepare_f …
asked Feb 10 '19 by Savco
2
votes
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What if there are multiple goals? For example, let's consider the bit-flipping environment as described in the paper HER with one small change: now, the goal is not some specific configuration, but le …
asked Mar 19 '19 by Savco
3
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In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the DRQN is described as DQN with the first post-convolutional fully-connected layer replaced by a recurrent LSTM. I have DQN imp …
asked Apr 8 '19 by Savco