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Implementations of popular deep reinforcement learning algorithms

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Implementations of popular deep reinforcement learning algorithms

TODO

  • batch-norm not working in eval mode
  • ideas from atari preprocessing
  • normalize input
  • optimize for speed
  • 4-frames stack
  • plot grad dist/grad norm
  • plot different losses
  • plot more metrics (from shultz presentation)
  • mean by time
  • remove float casts
  • refactor rollout to use s_prime at every step
  • normalize input
  • use record episode stats
  • merge wrappers and transforms
  • make layers shared between versions
  • check all conv paddings
  • 5 step horizon
  • use activation for value prediction
  • add action to obs
  • advantage normalization
  • td(0)
  • exp replay
  • td(lambda)
  • mpi
  • a3c
  • compute running mean/std of metrics
  • rename meta to info

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