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116 lines
4.3 KiB
Python
116 lines
4.3 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 os
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from collections import OrderedDict
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from typing import Union
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import pygame
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from pandas import to_pickle
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from rl_coach.agents.agent import Agent
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from rl_coach.agents.bc_agent import BCNetworkParameters
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from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
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from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
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from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, InputEmbedderParameters, EmbedderScheme, \
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AgentParameters
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from rl_coach.core_types import ActionInfo
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from rl_coach.exploration_policies.e_greedy import EGreedyParameters
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from rl_coach.logger import screen
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
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class HumanAlgorithmParameters(AlgorithmParameters):
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def __init__(self):
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super().__init__()
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class HumanNetworkParameters(NetworkParameters):
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def __init__(self):
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super().__init__()
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self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
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self.input_embedders_parameters['observation'].scheme = EmbedderScheme.Medium
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self.middleware_parameters = FCMiddlewareParameters()
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self.heads_parameters = [PolicyHeadParameters()]
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self.loss_weights = [1.0]
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self.optimizer_type = 'Adam'
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self.batch_size = 32
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self.replace_mse_with_huber_loss = False
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self.create_target_network = False
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class HumanAgentParameters(AgentParameters):
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def __init__(self):
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super().__init__(algorithm=HumanAlgorithmParameters(),
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exploration=EGreedyParameters(),
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memory=EpisodicExperienceReplayParameters(),
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networks={"main": BCNetworkParameters()})
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@property
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def path(self):
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return 'rl_coach.agents.human_agent:HumanAgent'
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class HumanAgent(Agent):
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def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
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super().__init__(agent_parameters, parent)
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self.clock = pygame.time.Clock()
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self.max_fps = int(self.ap.visualization.max_fps_for_human_control)
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self.env = None
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def init_environment_dependent_modules(self):
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super().init_environment_dependent_modules()
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self.env = self.parent_level_manager._real_environment
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screen.log_title("Human Control Mode")
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available_keys = self.env.get_available_keys()
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if available_keys:
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screen.log("Use keyboard keys to move. Press escape to quit. Available keys:")
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screen.log("")
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for action, key in self.env.get_available_keys():
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screen.log("\t- {}: {}".format(action, key))
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screen.separator()
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def train(self):
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return 0
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def choose_action(self, curr_state):
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action = ActionInfo(self.env.get_action_from_user(), action_value=0)
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action = self.output_filter.reverse_filter(action)
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# keep constant fps
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self.clock.tick(self.max_fps)
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if not self.env.renderer.is_open:
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self.save_replay_buffer_and_exit()
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return action
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def save_replay_buffer_and_exit(self):
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replay_buffer_path = os.path.join(self.agent_logger.experiments_path, 'replay_buffer.p')
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self.memory.tp = None
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to_pickle(self.memory, replay_buffer_path)
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screen.log_title("Replay buffer was stored in {}".format(replay_buffer_path))
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exit()
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def log_to_screen(self):
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# log to screen
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log = OrderedDict()
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log["Episode"] = self.current_episode
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log["Total reward"] = round(self.total_reward_in_current_episode, 2)
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log["Steps"] = self.total_steps_counter
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screen.log_dict(log, prefix="Recording")
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