From ed1f9cc4699c9fc1a66f8d6e33b8e4693dbee07e Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Sun, 1 Sep 2019 18:58:04 -0400
Subject: [PATCH] fixing learning issues
---
torch_training/training_navigation.py | 10 +++++++---
1 file changed, 7 insertions(+), 3 deletions(-)
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index 4f99b48..3ed4840 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -125,6 +125,7 @@ def main(argv):
# Build agent specific observations
for a in range(env.get_num_agents()):
agent_obs[a] = normalize_observation(obs[a], observation_radius=10)
+ agent_obs_buffer[a] = agent_obs[a].copy()
# Reset score and done
score = 0
@@ -136,11 +137,13 @@ def main(argv):
for a in range(env.get_num_agents()):
if env.agents[a].speed_data['position_fraction'] < 0.001:
register_action_state[a] = True
+ action = agent.act(agent_obs[a], eps=eps)
+ action_prob[action] += 1
+ if step == 0:
+ agent_action_buffer[a] = action
else:
register_action_state[a] = False
-
- action = agent.act(agent_obs[a], eps=eps)
- action_prob[action] += 1
+ action = 0
action_dict.update({a: action})
# Environment step
@@ -150,6 +153,7 @@ def main(argv):
for a in range(env.get_num_agents()):
agent_next_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
cummulated_reward[a] += all_rewards[a]
+
# Update replay buffer and train agent
for a in range(env.get_num_agents()):
if done[a]:
--
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