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PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN

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reinforcement-learning reinforcement-learning-algorithms temporal-differencing-learning maximum-entropy deep-q-learning deep-reinforcement-learning munchausen-reinforcement-learning

munchausen-rl's Introduction

Munchause-RL

PyTorch implementation of the M-DQN algorithm based on the paper Munchause Reinforcement Learning.

For a short introduction check out the Medium Article!

Implementations

Discrete Action Space:

Continuous Action Space:

M-SAC

Changes to the Paper

Compared to the original algorithm I did some changes:

Instead of doing a hard update every 8000 frames I implemented a soft-update. By personal experience this worked better.

Results

Comparison runs between M-DQN and DQN for the CartPole-v0 environment and LunarLander-v2.

Comparison of IQN and M-IQN for LunarLander-v2

Comparison IQN and M-IQN for Breakout

munchausen-rl's People

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munchausen-rl's Issues

Could you please add more support to atari environment?

Hello,
Could you please add more support to atari environment? Such as 'BreakoutNoFrameskip-v4', 'BreakoutDeterministic-v4'....

Wrong value in call to F.softmax

Should F.softmax(Q_targets_next, dim=1) be F.softmax(Q_targets_next / entropy_tau, dim=1) instead?

munchausen_addon and action

Hello!Excuse me！I used your code in another environment, but I encountered difficulties. Action is a decimal array,, so how to rewrite the " munchausen_addon = log_pi.gather(1, actions)" line of code! And the action space is very large! By the way, these lines of code are:adding the scald log-policy to the immediate reward
looking forward to your reply！

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