Comments (1)
It accounts for the distribution of states the agent would visit in the environment. If it visits a state less often, it should spend less model capacity on maximizing the value of that state. More importantly, if a state is never visited, because we're asking the model to predict beyond the episode end, we shouldn't train on that state at all because the model output could be anything in that case. That comes from maximizing the expected value under the policy. In a model-free agent, you sample states from the replay buffer so they already approximately follow the stationary distribution of the agent.
from dreamer.
Related Issues (20)
- A question about reward and observation pairing in wrapper HOT 2
- Tensorflow-probability version HOT 2
- Invalid one-hot action with Google Research football environment HOT 1
- lost of file 'dm_control' HOT 3
- difference between "CheetahRun-v0" on DM vs "half-cheetah-v2" on Mujuco HOT 1
- Spikes in Loss? HOT 2
- Runtime performance HOT 1
- Free nats over batch and time dimension? HOT 1
- Differences in free nats clipping between Dreamer, early and final PlaNet implementation HOT 2
- What is this line for? HOT 1
- How to run on short episodes? HOT 2
- slow in atari tasks HOT 2
- my.hackmit.org Can't register HOT 1
- AttributeError: 'MirroredStrategy' object has no attribute 'experimental_run_v2' HOT 1
- the code is running without any results and output HOT 5
- freenats inconsistent with tf1 repo HOT 1
- Can't reproduce results in some environments HOT 3
- Provided scores don't match the results HOT 2
- KL clipping: before or after averaging? HOT 1
- Different std of models
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from dreamer.