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[rllib] Fix eval.py -> rollout.py (#1650)

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Richard Liaw
2018-03-04 14:59:16 -08:00
committed by GitHub
parent a683cf2c70
commit 061e435411
1 changed files with 12 additions and 12 deletions
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doc/source/rllib.rst
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@@ -113,12 +113,12 @@ An example of evaluating a previously trained DQN agent is as follows:
.. code-block:: bash
python ray/python/ray/rllib/eval.py \
python ray/python/ray/rllib/rollout.py \
~/ray_results/default/DQN_CartPole-v0_0upjmdgr0/checkpoint-1 \
--run DQN --env CartPole-v0
The ``eval.py`` helper script reconstructs a DQN agent from the checkpoint
The ``rollout.py`` helper script reconstructs a DQN agent from the checkpoint
located at ``~/ray_results/default/DQN_CartPole-v0_0upjmdgr0/checkpoint-1``
and renders its behavior in the environment specified by ``--env``.
@@ -244,12 +244,12 @@ Multi-Agent Models
~~~~~~~~~~~~~~~~~~
RLlib supports multi-agent training with PPO. Currently it supports both
shared, i.e. all agents have the same model, and non-shared multi-agent models. However, it only supports shared
rewards and does not yet support individual rewards for each agent.
rewards and does not yet support individual rewards for each agent.
While Generalized Advantage Estimation is supported in multiagent scenarios,
it is assumed that it possible for the estimator to access the observations of
all of the agents.
While Generalized Advantage Estimation is supported in multiagent scenarios,
it is assumed that it possible for the estimator to access the observations of
all of the agents.
Important config parameters are described below
@@ -261,16 +261,16 @@ Important config parameters are described below
"multiagent_act_shapes": [1, 1], # length of each action space
"multiagent_shared_model": True, # whether the model should be shared
# list of dimensions of multiagent feedforward nets
"multiagent_fcnet_hiddens": [[32, 32]] * 2}
"multiagent_fcnet_hiddens": [[32, 32]] * 2}
config["model"].update({"custom_options": options})
For a full example of a multiagent model in code, see the
`MultiAgent Pendulum <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/multiagent_mountaincar.py>`__.
For a full example of a multiagent model in code, see the
`MultiAgent Pendulum <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/multiagent_mountaincar.py>`__.
The ``MultiAgentPendulumEnv`` defined there operates
over a composite (Tuple) enclosing a list of Boxes; each Box represents the
observation of an agent. The action space is a list of Discrete actions, each
over a composite (Tuple) enclosing a list of Boxes; each Box represents the
observation of an agent. The action space is a list of Discrete actions, each
element corresponding to half of the total torque. The environment will return a list of actions
that can be iterated over and applied to each agent.
that can be iterated over and applied to each agent.
External Data API
~~~~~~~~~~~~~~~~~