to support TF version < 1.5
to support rmsprop optimizer in Impala
Before TF1.5, tf.reduce_sum() and tf.reduce_max() has an argument keep_dims which has been renamed as keepdims in later versions.
In the original paper of Impala, they use rmsprop algorithm to optimize the model. We'd better also support it so that users can reproduce their experiments. Without any tuning, say that using the same hyper-parameters as AdamOptimizer, it reaches "episode_reward_mean": 19.083333333333332 in Pong after consume 3,610,350 samples.
## What do these changes do?
**Vectorized envs**: Users can either implement `VectorEnv`, or alternatively set `num_envs=N` to auto-vectorize gym envs (this vectorizes just the action computation part).
```
# CartPole-v0 on single core with 64x64 MLP:
# vector_width=1:
Actions per second 2720.1284458322966
# vector_width=8:
Actions per second 13773.035334888269
# vector_width=64:
Actions per second 37903.20472563333
```
**Async envs**: The more general form of `VectorEnv` is `AsyncVectorEnv`, which allows agents to execute out of lockstep. We use this as an adapter to support `ServingEnv`. Since we can convert any other form of env to `AsyncVectorEnv`, utils.sampler has been rewritten to run against this interface.
**Policy serving**: This provides an env which is not stepped. Rather, the env executes in its own thread, querying the policy for actions via `self.get_action(obs)`, and reporting results via `self.log_returns(rewards)`. We also support logging of off-policy actions via `self.log_action(obs, action)`. This is a more convenient API for some use cases, and also provides parallelizable support for policy serving (for example, if you start a HTTP server in the env) and ingest of offline logs (if the env reads from serving logs).
Any of these types of envs can be passed to RLlib agents. RLlib handles conversions internally in CommonPolicyEvaluator, for example:
```
gym.Env => rllib.VectorEnv => rllib.AsyncVectorEnv
rllib.ServingEnv => rllib.AsyncVectorEnv
```
* working multi action distribution and multiagent model
* currently working but the splits arent done in the right place
* added shared models
* added categorical support and mountain car example
* now compatible with generalized advantage estimation
* working multiagent code with discrete and continuous example
* moved reshaper to utils
* code review changes made, ppo action placeholder moved to model catalog, all multiagent code moved out of fcnet
* added examples in
* added PEP8 compliance
* examples are mostly pep8 compliant
* removed all flake errors
* added examples to jenkins tests
* fixed custom options bug
* added lines to let docker file find multiagent tests
* shortened example run length
* corrected nits
* fixed flake errors
* docs
* Update README.rst
* Sat Dec 30 15:23:49 PST 2017
* comments
* Sun Dec 31 23:33:30 PST 2017
* Sun Dec 31 23:33:38 PST 2017
* Sun Dec 31 23:37:46 PST 2017
* Sun Dec 31 23:39:28 PST 2017
* Sun Dec 31 23:43:05 PST 2017
* Sun Dec 31 23:51:55 PST 2017
* Sun Dec 31 23:52:51 PST 2017
