Comments (1)
If you read the original A3C paper, you will see that they introduce entropy regularisation to encourage a more uniform distribution of actions with A3C (which can be increased from the default in this code using the -entropyBeta <value>
option).
Note that RL problems are complex, and you will need to adjust many hyperparameters, such as the learning rate or the discount factor (gamma), to fit your problem. In many DeepMind papers they mention doing hundreds of experiments to find the appropriate set of hyperparameters, so unless you are trying to replicate results from a paper with known hyperparameters, expect that you might have to do the same.
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Related Issues (20)
- Implement Memory Q-networks
- Implement Retrace(λ)
- Finish prioritised experience replay HOT 2
- Allow non-visual environments
- Can I convert rank-based prioritized experience replay to a python version HOT 2
- Async A3C Network Outputs NaN HOT 4
- Load models like environments HOT 2
- Disagreements with the async paper HOT 2
- Possible improvements on speeding up HOT 1
- problem in Agent.lua HOT 1
- gnuplots memory unreleased HOT 1
- Why is the current sharedRmsprop thread safe? HOT 2
- Implement optimality tightening HOT 8
- What is the actual performance? HOT 7
- Refactor DQN train function into separate functions
- Partition number and segments HOT 1
- How to process with the salient map? HOT 4
- actor-critic based HOT 2
- About A3C HOT 1
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