This refactors the RLlib sampler to support multi-agent environments. The main changes were:
AsyncVectorEnv now produces dicts of env_id -> agent_id -> value rather than env_id -> value. This lets it model both vectorized and multi-agent envs (or both).
The sampler class operates over the above nested dict structure for all envs. Single agent envs just return a dict with one agent_id=single_agent.
When sample() is called on a policy evaluator, in the single agent case we return a SampleBatch, otherwise we return a MultiAgentBatch (which is a list of sample batches per policy).
Left for another PR:
Exposing multi-agent in the public interfaces.
Optimizations such as evaluating multiple policies in one TF run.
## 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
```
