def get_agent_connectors_from_config(
ctx: ConnectorContext,
config: TrainerConfigDict,
) -> AgentConnectorPipeline:
connectors = []
if config["clip_rewards"] is True:
connectors.append(ClipRewardAgentConnector(ctx, sign=True))
elif type(config["clip_rewards"]) == float:
connectors.append(
ClipRewardAgentConnector(ctx, limit=abs(config["clip_rewards"]))
)
if not config["_disable_preprocessor_api"]:
connectors.append(ObsPreprocessorConnector(ctx))
connectors.extend(
[
StateBufferConnector(ctx),
ViewRequirementAgentConnector(ctx),
FlattenDataAgentConnector(ctx), # Creates batch dimension.
]
)
return AgentConnectorPipeline(ctx, connectors)
def get_agent_connectors_from_config(
config: TrainerConfigDict,
obs_space: gym.Space) -> AgentConnectorPipeline:
connectors = [FlattenDataAgentConnector()]
if config["clip_rewards"] is True:
connectors.append(ClipRewardAgentConnector(sign=True))
elif type(config["clip_rewards"]) == float:
connectors.append(
ClipRewardAgentConnector(limit=abs(config["clip_rewards"])))
return AgentConnectorPipeline(connectors)
def test_connector_pipeline(self):
ctx = ConnectorContext()
connectors = [ClipRewardAgentConnector(ctx, False, 1.0)]
pipeline = AgentConnectorPipeline(ctx, connectors)
name, params = pipeline.to_config()
restored = get_connector(ctx, name, params)
self.assertTrue(isinstance(restored, AgentConnectorPipeline))
self.assertTrue(isinstance(restored.connectors[0], ClipRewardAgentConnector))
def test_clip_reward_connector(self):
ctx = ConnectorContext()
c = ClipRewardAgentConnector(ctx, limit=2.0)
name, params = c.to_config()
self.assertEqual(name, "ClipRewardAgentConnector")
self.assertAlmostEqual(params["limit"], 2.0)
restored = get_connector(ctx, name, params)
self.assertTrue(isinstance(restored, ClipRewardAgentConnector))
d = AgentConnectorDataType(
0,
1,
{
SampleBatch.REWARDS: 5.8,
},
)
clipped = restored(ac_data=d)
self.assertEqual(len(clipped), 1)
self.assertEqual(clipped[0].data[SampleBatch.REWARDS], 2.0)