Reinforcement learning algorithms implemented using Keras and OpenAI Gym
This repository aims to contain the latest reinforcement learning algorithms implemented using Tensorflow, Keras and OpenAI Gym.
Currently, A3C has been implemented.
pip install -r requirements.txt
agent = A3CAgent(num_actions, lambda: model)
agent.train(env_name)
Tensorboard Logging
tensorboard --logdir=out --reload_interval=2
Sources:
While trying the a3c example provided I encountered the following error:
Training model
Training ACAgentRunner...
[2017-04-10 16:30:50,699] Making new env: CartPole-v0
Training ACAgentRunner...
[2017-04-10 16:30:51,751] Making new env: CartPole-v0
Training ACAgentRunner...
[2017-04-10 16:30:52,808] Making new env: CartPole-v0
Training ACAgentRunner...
[2017-04-10 16:30:53,878] Making new env: CartPole-v0
Training ACAgentRunner...
[2017-04-10 16:30:54,935] Making new env: CartPole-v0
Training ACAgentRunner...
[2017-04-10 16:30:56,057] Making new env: CartPole-v0
Training ACAgentRunner...
[2017-04-10 16:30:57,118] Making new env: CartPole-v0
Training ACAgentRunner...
Exception in thread Thread-20:
Traceback (most recent call last):
File "/usr/lib/python3.5/threading.py", line 914, in _bootstrap_inner
self.run()
File "/usr/lib/python3.5/threading.py", line 862, in run
self._target(*self._args, **self._kwargs)
File "/home/jsteffen/rl-master/rl/util.py", line 30, in save_worker
agent.save(sess)
File "/home/jsteffen/rl-master/rl/a3c.py", line 223, in save
self.model.model.save_weights(self.model_path + '/model_' + str(self.save_count) + '.h5')
File "/home/jsteffen/venv3/lib/python3.5/site-packages/keras/engine/topology.py", line 2465, in save_weights
save_weights_to_hdf5_group(f, self.layers)
File "/home/jsteffen/venv3/lib/python3.5/site-packages/keras/engine/topology.py", line 2715, in save_weights_to_hdf5_group
weight_values = K.batch_get_value(symbolic_weights)
File "/home/jsteffen/venv3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1977, in batch_get_value
return get_session().run(ops)
File "/home/jsteffen/venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/home/jsteffen/venv3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 905, in _run
raise RuntimeError('The Session graph is empty. Add operations to the '
RuntimeError: The Session graph is empty. Add operations to the graph before calling run().
Hi, I see that you have already thought of implementing some kind of "recurrentness" using the time_steps parameter.
Is it possible to train a stateful model (i.e., having data in shape as (batch_size, time_steps, features)) and resetting the state of, e.g., an lstm layer, for experience replay?
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