Changing how DQN agent explores
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Hi,
I'm using a DQN agent with epsilon-greedy exploration. The problem is that my agent sees state 1 99% of the time, so it never learns to act in other states. By the time it learns to get to state 2 from state 1, epsilon has already decayed significantly and the agent gets stuck taking a sub-optimal action in state 2. Is there a way to implement some other form of exploration, like using a Boltzmann distribution? Thanks for your time.
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Tanay Gupta
2021-7-13
Can you give a brief description of the states and the respective transitions?
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