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Implementation of Sparse Graphical Memory (SGM) and Search on the Replay Buffer (SoRB) in PyTorch

License: Apache License 2.0

Python 100.00%
ddpg deep-reinforcement-learning pytorch reinforcement-learning rl search-on-the-replay-buffer sparse-graphical-memory

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sgm-sorb's Issues

Problem with test run

Hi, thanks for the awesome implementation, really appreciate the effort.

However, when I try to run the base case. I got this problem:

Traceback (most recent call last):
  File "test/run_PointEnv.py", line 10, in <module>
    env = env_load_fn(cfg.env.env_name, cfg.env.max_episode_steps,
  File "test/pud/envs/simple_navigation_env.py", line 568, in env_load_fn
    env = PointEnv(walls=environment_name,
  File "test/pud/envs/simple_navigation_env.py", line 327, in __init__
    self.reset()
  File "test/pud/envs/simple_navigation_env.py", line 341, in reset
    self.state = self._sample_empty_state()
  File "test/pud/envs/simple_navigation_env.py", line 337, in _sample_empty_state
    assert not self._is_blocked(state)
AssertionError

Seems this is an environment problem, not sure how to solve it.

Thanks in advance for any help you can offer!

Unable to run example

Hey,
I am trying to run the example but am unable to do so because the standard packages don't work and have breaking changes as their versions have been upgraded.
Could you tell me:

  1. What were the package dependencies and their versions used?
  2. What is the latest research in this field or extending sgm-sorb futher (I am still exploring the space)

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