diff --git a/reinforcement_learning/dddqn_policy.py b/reinforcement_learning/dddqn_policy.py
index 2cf7ad25e4f581c86cd53bc49671669b8820ab8f..1c323c366c7ff6b08baf1ff2d7d9ceee389bbd57 100644
--- a/reinforcement_learning/dddqn_policy.py
+++ b/reinforcement_learning/dddqn_policy.py
@@ -67,7 +67,7 @@ class DDDQNPolicy(Policy):
else:
return random.choice(np.arange(self.action_size))
- def step(self, state, action, reward, next_state, done):
+ def step(self, handle, state, action, reward, next_state, done):
assert not self.evaluation_mode, "Policy has been initialized for evaluation only."
# Save experience in replay memory
diff --git a/reinforcement_learning/multi_agent_training.py b/reinforcement_learning/multi_agent_training.py
index 1d99a9b80d51d8ff34991efbeb802af2a373a292..6f250a83fb830afef842037c312629b99cdb78c1 100755
--- a/reinforcement_learning/multi_agent_training.py
+++ b/reinforcement_learning/multi_agent_training.py
@@ -18,13 +18,14 @@ from flatland.envs.schedule_generators import sparse_schedule_generator
from flatland.utils.rendertools import RenderTool
from torch.utils.tensorboard import SummaryWriter
+from reinforcement_learning.ppo.ppo_agent import PPOAgent
+
base_dir = Path(__file__).resolve().parent.parent
sys.path.append(str(base_dir))
from utils.timer import Timer
from utils.observation_utils import normalize_observation
-from utils.fast_tree_obs import FastTreeObs, fast_tree_obs_check_agent_deadlock
-from reinforcement_learning.dddqn_policy import DDDQNPolicy
+from utils.fast_tree_obs import FastTreeObs
try:
import wandb
@@ -171,8 +172,8 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
completion_window = deque(maxlen=checkpoint_interval)
# Double Dueling DQN policy
- policy = DDDQNPolicy(state_size, action_size, train_params)
-
+ # policy = DDDQNPolicy(state_size, action_size, train_params)
+ policy = PPOAgent(state_size, action_size, n_agents)
# Load existing policy
if train_params.load_policy is not "":
policy.load(train_params.load_policy)
@@ -226,6 +227,7 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
train_env_params.n_agents = episode_idx % n_agents + 1
train_env = create_rail_env(train_env_params, tree_observation)
obs, info = train_env.reset(regenerate_rail=True, regenerate_schedule=True)
+ policy.reset()
reset_timer.end()
if train_params.render:
@@ -288,18 +290,10 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
if update_values[agent] or done['__all__']:
# Only learn from timesteps where somethings happened
learn_timer.start()
- call_step = True
-
- if not (agent_obs[agent][7] == 1 or agent_obs[agent][8] == 1 or agent_obs[agent][4] == 1):
- if action_dict.get(agent) == RailEnvActions.MOVE_FORWARD:
- call_step = np.random.random() < 0.1
- if fast_tree_obs_check_agent_deadlock(agent_obs[agent]):
- all_rewards[agent] -= 10
- call_step = True
- if call_step:
- policy.step(agent_prev_obs[agent], agent_prev_action[agent], all_rewards[agent],
- agent_obs[agent],
- done[agent])
+ policy.step(agent,
+ agent_prev_obs[agent], agent_prev_action[agent], all_rewards[agent],
+ agent_obs[agent],
+ done[agent])
learn_timer.end()
agent_prev_obs[agent] = agent_obs[agent].copy()
@@ -444,7 +438,6 @@ def eval_policy(env, tree_observation, policy, train_params, obs_params):
score = 0.0
obs, info = env.reset(regenerate_rail=True, regenerate_schedule=True)
-
final_step = 0
for step in range(max_steps - 1):
diff --git a/reinforcement_learning/policy.py b/reinforcement_learning/policy.py
index b8714d1a2fd8e085d4e9e00c48c7362846e8ed87..b605aa3ddaf43ad1a496e44a3fac367be4bd8234 100644
--- a/reinforcement_learning/policy.py
+++ b/reinforcement_learning/policy.py
@@ -1,5 +1,5 @@
class Policy:
- def step(self, state, action, reward, next_state, done):
+ def step(self, handle, state, action, reward, next_state, done):
raise NotImplementedError
def act(self, state, eps=0.):
@@ -16,3 +16,12 @@ class Policy:
def end_step(self):
pass
+
+ def load_replay_buffer(self, filename):
+ pass
+
+ def test(self):
+ pass
+
+ def reset(self):
+ pass
\ No newline at end of file
diff --git a/reinforcement_learning/ppo/ppo_agent.py b/reinforcement_learning/ppo/ppo_agent.py
index e43cb3080bf5187e148f0b06e69749f46e840e9e..663a05acb42fd5a919f4eff0c3c45146d9bbd471 100644
--- a/reinforcement_learning/ppo/ppo_agent.py
+++ b/reinforcement_learning/ppo/ppo_agent.py
@@ -20,7 +20,7 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class PPOAgent(Policy):
- def __init__(self, state_size, action_size, num_agents, env):
+ def __init__(self, state_size, action_size, num_agents):
self.action_size = action_size
self.state_size = state_size
self.num_agents = num_agents
@@ -31,7 +31,7 @@ class PPOAgent(Policy):
self.memory = ReplayBuffer(BUFFER_SIZE)
self.t_step = 0
self.loss = 0
- self.env = env
+ self.num_agents = num_agents
def reset(self):
self.finished = [False] * len(self.episodes)
@@ -39,21 +39,11 @@ class PPOAgent(Policy):
# Decide on an action to take in the environment
- def act(self, handle, state, eps=None):
- if True:
- self.policy.eval()
- with torch.no_grad():
- output = self.policy(torch.from_numpy(state).float().unsqueeze(0).to(device))
- return Categorical(output).sample().item()
-
- # Epsilon-greedy action selection
- if random.random() > eps:
- self.policy.eval()
- with torch.no_grad():
- output = self.policy(torch.from_numpy(state).float().unsqueeze(0).to(device))
- return Categorical(output).sample().item()
- else:
- return random.choice(np.arange(self.action_size))
+ def act(self, state, eps=None):
+ self.policy.eval()
+ with torch.no_grad():
+ output = self.policy(torch.from_numpy(state).float().unsqueeze(0).to(device))
+ return Categorical(output).sample().item()
# Record the results of the agent's action and update the model
def step(self, handle, state, action, reward, next_state, done):
diff --git a/reinforcement_learning/single_agent_training.py b/reinforcement_learning/single_agent_training.py
index 236d1a76cbbff9be26612e81bf24265886acbab8..bfcc88656c8b37a8c09e72b51701d0750cf7f238 100644
--- a/reinforcement_learning/single_agent_training.py
+++ b/reinforcement_learning/single_agent_training.py
@@ -146,7 +146,9 @@ def train_agent(n_episodes):
for agent in range(env.get_num_agents()):
# Only update the values when we are done or when an action was taken and thus relevant information is present
if update_values or done[agent]:
- policy.step(agent_prev_obs[agent], agent_prev_action[agent], all_rewards[agent], agent_obs[agent], done[agent])
+ policy.step(agent,
+ agent_prev_obs[agent], agent_prev_action[agent], all_rewards[agent],
+ agent_obs[agent], done[agent])
agent_prev_obs[agent] = agent_obs[agent].copy()
agent_prev_action[agent] = action_dict[agent]