pre-release 0.10.0

rl_coach/agents/human_agent.py

@@ -0,0 +1,115 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+from collections import OrderedDict
+from typing import Union
+
+import pygame
+from rl_coach.agents.agent import Agent
+from rl_coach.agents.bc_agent import BCNetworkParameters
+from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, InputEmbedderParameters, EmbedderScheme, \
+    AgentParameters
+from rl_coach.core_types import ActionInfo
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from pandas import to_pickle
+
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.logger import screen
+
+
+class HumanAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+
+
+class HumanNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.input_embedders_parameters['observation'].scheme = EmbedderScheme.Medium
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [PolicyHeadParameters()]
+        self.loss_weights = [1.0]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 32
+        self.replace_mse_with_huber_loss = False
+        self.create_target_network = False
+
+
+class HumanAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=HumanAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"main": BCNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.human_agent:HumanAgent'
+
+
+class HumanAgent(Agent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+        self.clock = pygame.time.Clock()
+        self.max_fps = int(self.ap.visualization.max_fps_for_human_control)
+        self.env = None
+
+    def init_environment_dependent_modules(self):
+        super().init_environment_dependent_modules()
+        self.env = self.parent_level_manager._real_environment
+        screen.log_title("Human Control Mode")
+        available_keys = self.env.get_available_keys()
+        if available_keys:
+            screen.log("Use keyboard keys to move. Press escape to quit. Available keys:")
+            screen.log("")
+            for action, key in self.env.get_available_keys():
+                screen.log("\t- {}: {}".format(action, key))
+            screen.separator()
+
+    def train(self):
+        return 0
+
+    def choose_action(self, curr_state):
+        action = ActionInfo(self.env.get_action_from_user(), action_value=0)
+        action = self.output_filter.reverse_filter(action)
+
+        # keep constant fps
+        self.clock.tick(self.max_fps)
+
+        if not self.env.renderer.is_open:
+            self.save_replay_buffer_and_exit()
+
+        return action
+
+    def save_replay_buffer_and_exit(self):
+        replay_buffer_path = os.path.join(self.agent_logger.experiments_path, 'replay_buffer.p')
+        self.memory.tp = None
+        to_pickle(self.memory, replay_buffer_path)
+        screen.log_title("Replay buffer was stored in {}".format(replay_buffer_path))
+        exit()
+
+    def log_to_screen(self):
+        # log to screen
+        log = OrderedDict()
+        log["Episode"] = self.current_episode
+        log["Total reward"] = round(self.total_reward_in_current_episode, 2)
+        log["Steps"] = self.total_steps_counter
+        screen.log_dict(log, prefix="Recording")