tf-agents throws ValueError: Layer dense layer expects 1 input(s), but it received 4 input tensors when using custom environment with OpenAI Gym

• agents

  11 2,731 8.0 Python

  TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

  

im currently toying aroud with openai gym and tf-agents for use of a project that i want to start in the near future and am experimenting with setting dictionaries using spaces.Dict as my observational space but i am running into issues getting that working with tf-agents. i've been following this guide which uses the built-in cartpole env packaged with gym to train a DQN agent. the guide looks to be agnostic to any environment when it comes to setting up the agent though, but when i initialize the agent Tensorflow throws an error:

• gym

  96 33,905 0.0 Python

  A toolkit for developing and comparing reinforcement learning algorithms.

  

Well it seems it doesn't flatten anything, just passes OrderedDict as input dense. Not sure but apparently it's keras that makes that a list of tensors. You can dig around places like https://github.com/tensorflow/agents/blob/v0.8.0/tf_agents/networks/network.py https://github.com/tensorflow/agents/blob/v0.8.0/tf_agents/agents/dqn/dqn_agent.py https://github.com/openai/gym/blob/master/gym/spaces/dict.py if you want to be really sure.

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