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Flatland
neurips2020-flatland-baselines
Commits
817e5040
Commit
817e5040
authored
Aug 25, 2020
by
nilabha
Browse files
Rollout scripts updates
parent
ed021c4d
Changes
2
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Inline
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rollout.py
View file @
817e5040
...
...
@@ -15,9 +15,9 @@ import ray
from
ray.rllib.agents.registry
import
get_agent_class
from
ray.rllib.env
import
MultiAgentEnv
from
ray.rllib.env.base_env
import
_DUMMY_AGENT_ID
from
ray.rllib.evaluation.episode
import
_flatten_action
#
from ray.rllib.evaluation.episode import _flatten_action
# ray 0.8.4
from
ray.rllib.policy.sample_batch
import
DEFAULT_POLICY_ID
#
from ray.rllib.utils.space_utils import flatten_to_single_ndarray
from
ray.rllib.utils.space_utils
import
flatten_to_single_ndarray
# ray 0.8.5
from
ray.tune.utils
import
merge_dicts
from
utils.loader
import
load_envs
,
load_models
...
...
@@ -324,8 +324,8 @@ def rollout(agent,
state_init
=
{
p
:
m
.
get_initial_state
()
for
p
,
m
in
policy_map
.
items
()}
use_lstm
=
{
p
:
len
(
s
)
>
0
for
p
,
s
in
state_init
.
items
()}
action_init
=
{
#
p: flatten_to_single_ndarray(m.action_space.sample()) # ray 0.8.5
p
:
_flatten_action
(
m
.
action_space
.
sample
())
# ray 0.8.4
p
:
flatten_to_single_ndarray
(
m
.
action_space
.
sample
())
# ray 0.8.5
#
p: _flatten_action(m.action_space.sample()) # ray 0.8.4
for
p
,
m
in
policy_map
.
items
()
}
else
:
...
...
@@ -389,8 +389,8 @@ def rollout(agent,
prev_action
=
prev_actions
[
agent_id
],
prev_reward
=
prev_rewards
[
agent_id
],
policy_id
=
policy_id
)
#
a_action = flatten_to_single_ndarray(a_action) # ray 0.8.5
a_action
=
_flatten_action
(
a_action
)
# tuple actions # ray 0.8.4
a_action
=
flatten_to_single_ndarray
(
a_action
)
# ray 0.8.5
#
a_action = _flatten_action(a_action) # tuple actions # ray 0.8.4
action_dict
[
agent_id
]
=
a_action
prev_actions
[
agent_id
]
=
a_action
action
=
action_dict
...
...
@@ -425,7 +425,7 @@ def rollout(agent,
episode_score
=
sum
(
agents_score
.
values
())
simulation_rewards
.
append
(
episode_score
)
simulation_rewards_normalized
.
append
(
episode_score
/
(
episode_max_steps
+
episode_num_agents
))
simulation_rewards_normalized
.
append
(
episode_score
/
(
episode_max_steps
*
episode_num_agents
))
simulation_percentage_complete
.
append
(
float
(
len
(
agents_done
))
/
episode_num_agents
)
simulation_steps
.
append
(
episode_steps
)
...
...
rollout.sh
0 → 100644
View file @
817e5040
echo
"===================="
echo
"APEX TREE OBS"
echo
"===================="
# python rollout.py baselines/checkpoints/apex-tree-obs-small-v0-0/checkpoint_400/checkpoint-400 --run APEX --no-render --episodes 50 --env 'flatland_sparse' --config '{"env_config": {"seed":1000000000,"generator": "sparse_rail_generator", "generator_config": "small_v0", "observation": "tree", "observation_config": {"max_depth": 2, "shortest_path_max_depth": 30}}, "model": {"fcnet_activation": "relu", "fcnet_hiddens": [256, 256], "vf_share_layers": "True"}}'
# python rollout.py baselines/checkpoints/apex_tree_obs_small_v0-1/checkpoint_400/checkpoint-400 --run APEX --no-render --episodes 50 --env 'flatland_sparse' --config '{"env_config": {"seed":1000000000,"generator": "sparse_rail_generator", "generator_config": "small_v0", "observation": "tree", "observation_config": {"max_depth": 2, "shortest_path_max_depth": 30}}, "model": {"fcnet_activation": "relu", "fcnet_hiddens": [256, 256], "vf_share_layers": "True"}}'
# python rollout.py baselines/checkpoints/apex_tree_obs_small_v0-2/checkpoint_250/checkpoint-250 --run APEX --no-render --episodes 50 --env 'flatland_sparse' --config '{"env_config": {"seed":1000000000,"generator": "sparse_rail_generator", "generator_config": "small_v0", "observation": "tree", "observation_config": {"max_depth": 2, "shortest_path_max_depth": 30}}, "model": {"fcnet_activation": "relu", "fcnet_hiddens": [256, 256], "vf_share_layers": "True"}}'
\ No newline at end of file
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