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Adgudime/apex sac #32

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10d630e
Updated params for Pendulum
AdityaGudimella Aug 19, 2020
150f9ea
Tuned hyperparams for ApexSAC
AdityaGudimella Aug 24, 2020
703f544
Merge branch 'releases/0.8.5' into adgudime/apex_sac
AdityaGudimella Aug 24, 2020
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8 changes: 4 additions & 4 deletions rllib/agents/sac/apex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,15 +17,15 @@
"n_step": 1,
"num_gpus": 0,
"num_workers": 32,
"buffer_size": 2000000,
"learning_starts": 50000,
"buffer_size": 200000,
"learning_starts": 5000,
"train_batch_size": 512,
"rollout_fragment_length": 50,
"target_network_update_freq": 0,
"timesteps_per_iteration": 25000,
"timesteps_per_iteration": 1000,
"exploration_config": {"type": "PerWorkerEpsilonGreedy"},
"worker_side_prioritization": True,
"min_iter_time_s": 30,
"min_iter_time_s": 10,
"prioritized_replay": True,
},
)
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31 changes: 19 additions & 12 deletions rllib/optimizers/async_replay_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -474,20 +474,27 @@ def step(self):
except KeyError:
pass
else:
is_box_action_space = all((
hasattr(self.local_worker, "policy_map"),
self.local_worker.policy_map.get(
"default_policy", None
) is not None,
isinstance(
self.local_worker.policy_map[
"default_policy"
].action_space,
gym.spaces.Box
is_box_action_space = all(
(
hasattr(self.local_worker, "policy_map"),
self.local_worker.policy_map.get("default_policy", None)
is not None,
isinstance(
self.local_worker.policy_map[
"default_policy"
].action_space,
gym.spaces.Box,
),
)
))
)
if is_box_action_space:
batch["actions"] = batch["actions"].reshape((-1, 1))
# Reshape to (batch_size, action_space_dim)
action_space = self.local_worker.policy_map[
"default_policy"
].action_space
batch["actions"] = batch["actions"].reshape(
(-1, action_space.shape[0])
)
grad_out = self.local_worker.learn_on_batch(replay)
for pid, info in grad_out.items():
td_error = info.get(
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32 changes: 17 additions & 15 deletions rllib/tests/agents/parameters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -272,29 +272,31 @@ def astuple(self):
TestAgentParams.for_cart_pole(
algorithm=ContinuousActionSpaceAlgorithm.APEX_SAC,
config_updates={
"seed": 42,
"num_workers": 8,
"buffer_size": 200000,
"learning_starts": 6000,
"train_batch_size": 64,
"target_network_update_freq": 0,
"timesteps_per_iteration": 2500,
"min_iter_time_s": 3,
},
n_iter=200,
n_iter=100,
threshold=175.0,
),
TestAgentParams.for_pendulum(
algorithm=ContinuousActionSpaceAlgorithm.APEX_SAC,
config_updates={
"num_workers": 4,
"prioritized_replay": True,
"timesteps_per_iteration": 100,
"min_iter_time_s": 1,
"optimizer": {"num_replay_buffer_shards": 1},
"learning_starts": 0,
"num_workers": 8,
"exploration_config": {"type": "StochasticSampling"},
"no_done_at_end": True,
},
n_iter=200,
n_iter=100,
threshold=-350.0,
),
TestAgentParams.for_frameworks(
algorithm=ContinuousActionSpaceAlgorithm.APEX_SAC,
config_updates={
"num_workers": 8,
"exploration_config": {"type": "StochasticSampling"},
},
env="MountainCarContinuous-v0",
# TensorFlow returns Nan mean ep reward for the first few epoch
n_iter=100,
threshold=100.0,
),
TestAgentParams.for_pendulum(
algorithm=DiscreteActionSpaceAlgorithm.SAC,
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