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82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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from agents.value_optimization_agent import ValueOptimizationAgent
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from utils import RunPhase, Signal
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# Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf
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class NAFAgent(ValueOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.l_values = Signal("L")
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self.a_values = Signal("Advantage")
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self.mu_values = Signal("Action")
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self.v_values = Signal("V")
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self.signals += [self.l_values, self.a_values, self.mu_values, self.v_values]
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def learn_from_batch(self, batch):
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current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
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# TD error = r + discount*v_st_plus_1 - q_st
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v_st_plus_1 = self.main_network.target_network.predict(
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next_states,
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self.main_network.target_network.output_heads[0].V,
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squeeze_output=False,
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)
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TD_targets = np.expand_dims(rewards, -1) + (1.0 - np.expand_dims(game_overs, -1)) * self.tp.agent.discount * v_st_plus_1
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if len(actions.shape) == 1:
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actions = np.expand_dims(actions, -1)
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result = self.main_network.train_and_sync_networks({**current_states, 'output_0_0': actions}, TD_targets)
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total_loss = result[0]
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return total_loss
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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assert not self.env.discrete_controls, 'NAF works only for continuous control problems'
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# convert to batch so we can run it through the network
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# observation = np.expand_dims(np.array(curr_state['observation']), 0)
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naf_head = self.main_network.online_network.output_heads[0]
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action_values = self.main_network.online_network.predict(
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self.tf_input_state(curr_state),
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outputs=naf_head.mu,
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squeeze_output=False,
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)
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if phase == RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = action_values
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Q, L, A, mu, V = self.main_network.online_network.predict(
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{**self.tf_input_state(curr_state), 'output_0_0': action_values},
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outputs=[naf_head.Q, naf_head.L, naf_head.A, naf_head.mu, naf_head.V],
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)
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# store the q values statistics for logging
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self.q_values.add_sample(Q)
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self.l_values.add_sample(L)
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self.a_values.add_sample(A)
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self.mu_values.add_sample(mu)
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self.v_values.add_sample(V)
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action_value = {"action_value": Q}
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return action, action_value
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