Skip to content
This repository has been archived by the owner on Jun 13, 2020. It is now read-only.


Folders and files

Last commit message
Last commit date

Latest commit



61 Commits

Repository files navigation


for more general info see the blog post

for more advanced RL experiments (with less of a focus on sim-real transfer learning) see cartpoleplusplus

for info on the physical rover build see this google+ collection

building drivebot ROS package

drivebot has two ROS specific components that need to be built.

  • a ActionGivenState.srv service definition that describes how bots (real or simulated) interface with the NN policy
  • a TrainingExample.msg msg definition that describes training examples sent to the NN policy`
# build msg and service definitions
cd $ROOT_OF_CHECKOUT  # whatever this is...
cd ros_ws

# add various ROS related paths to environment variables
# (add this to .bashrc if required)
source $ROOT_OF_CHECKOUT/ros_ws/devel/setup.bash

running ros env

stdr sim

roslaunch stdr_launchers server_no_map.launch

load map and add three bots...

rosservice call /stdr_server/load_static_map "mapFile: '$PWD/maps/track1.yaml'"
rosrun stdr_robot robot_handler add $PWD/maps/pandora_robot.yaml 5 5 0
rosrun stdr_robot robot_handler add $PWD/maps/pandora_robot.yaml 5 5 0
rosrun stdr_robot robot_handler add $PWD/maps/pandora_robot.yaml 5 5 0

optionally run gui (for general sanity) and rviz (for odom visulations)

roslaunch stdr_gui stdr_gui.launch
roslaunch stdr_launchers rviz.launch

running sim

start external policy

the policy represents the decision making process for bots.

policy is run in seperate process so it can support >1 bots. given stdr's limitations of not being able to run faster-than-real time the quickest way to train (apart from experience replay) is having multiple bots running.

$ ./ --help
usage: [-h] [--policy POLICY] [--state-size STATE_SIZE]
                        [--q-discount Q_DISCOUNT]
                        [--q-learning-rate Q_LEARNING_RATE]
                        [--q-state-normalisation-squash Q_STATE_NORMALISATION_SQUASH]
                        [--gradient-clip GRADIENT_CLIP]
                        [--summary-log-dir SUMMARY_LOG_DIR]
                        [--summary-log-freq SUMMARY_LOG_FREQ]
                        [--target-network-update-freq TARGET_NETWORK_UPDATE_FREQ]
                        [--target-network-update-coeff TARGET_NETWORK_UPDATE_COEFF]

optional arguments:
  -h, --help            show this help message and exit
  --policy POLICY       what policy to use; Baseline / DiscreteQTablePolicy /
  --state-size STATE_SIZE
                        state size we expect from bots (dependent on their
                        sonar to state config)
  --q-discount Q_DISCOUNT
                        q table discount. 0 => ignore future possible rewards,
                        1 => assume q future rewards perfect. only applicable
                        for QTablePolicies.
  --q-learning-rate Q_LEARNING_RATE
                        q table learning rate. different interp between
                        discrete & nn policies
  --q-state-normalisation-squash Q_STATE_NORMALISATION_SQUASH
                        what power to raise sonar ranges to before
                        normalisation. <1 => explore (tends to uniform), >1 =>
                        exploit (tends to argmax). only applicable for
  --gradient-clip GRADIENT_CLIP
  --summary-log-dir SUMMARY_LOG_DIR
                        where to write tensorflow summaries (for the
                        tensorflow models)
  --summary-log-freq SUMMARY_LOG_FREQ
                        freq (in training examples) in which to write to
  --target-network-update-freq TARGET_NETWORK_UPDATE_FREQ
                        freq (in training examples) in which to flush core
                        network to target network
  --target-network-update-coeff TARGET_NETWORK_UPDATE_COEFF
                        affine coeff for target network update. 0 => no
                        update, 0.5 => mean of core/target, 1.0 => clobber
                        target completely

run one bot

$ ./ --help
usage: [-h] [--robot-id ROBOT_ID] [--max-episode-len MAX_EPISODE_LEN]
              [--num-episodes NUM_EPISODES]
              [--episode-log-file EPISODE_LOG_FILE]
              [--max-no-rewards-run-len MAX_NO_REWARDS_RUN_LEN]
              [--sonar-to-state SONAR_TO_STATE]
              [--state-history-length STATE_HISTORY_LENGTH]

optional arguments:
  -h, --help            show this help message and exit
  --robot-id ROBOT_ID
  --max-episode-len MAX_EPISODE_LEN
  --num-episodes NUM_EPISODES
  --episode-log-file EPISODE_LOG_FILE
                        where to write episode log jsonl
  --max-no-rewards-run-len MAX_NO_REWARDS_RUN_LEN
                        early stop episode if no +ve reward in this many steps
  --sonar-to-state SONAR_TO_STATE
                        what state tranformer to use; FurthestSonar /
                        OrderingSonars / StandardisedSonars
  --state-history-length STATE_HISTORY_LENGTH
                        if >1 wrap sonar-to-state in a StateHistory

common config includes


just go in direction of furthest sonar.

state is simply which sonar reads the furthest distance (0 for F, 1 for L and 2 for R) e.g. [0]

./ --policy Baseline
./ --robot-id 0 --sonar-to-state FurthestSonar
./ --robot-id 1 --sonar-to-state FurthestSonar


discrete state q table

q table with discrete states based on which sonar is reporting the furthest distance.

state is concat of history of last 4 readings; eg [0, 0, 1, 1]

./ --policy DiscreteQTablePolicy --q-state-normalisation-squash=50
./ --robot-id 0 --sonar-to-state FurthestSonar 
./ --robot-id 1 --sonar-to-state FurthestSonar 

continous state q learnt function

train a single layer NN using standardised sonar readings (mu and stddev derived from sample data for discrete state q table

./ --policy=NNQTablePolicy
--q-learning-rate=0.01 --q-state-normalisation-squash=50 --state-size=3
./ --robot-id=0 --sonar-to-state=StandardisedSonars


state history variants

all can be run with --state-history-length=N to maintain state as a history of the last N sonar readings. (i.e. stat is 3N floats, not just 3)

in the case of NNQTablePolicy we need to pass 3N as --state-size

training config

some config is expressed as ros parameters. these params are refreshed periodically by sim / trainer..

$ rosparam get /q_table_policy
{ discount: 0.9,                    # bellman RHS discount
  learning_rate: 0.1,               # sgd learning rate
  state_normalisation_squash: 1.0,  # action choosing squash; <1.0 (~0.01) => random choice, >1 (~50) => arg max
  summary_log_freq: 100,            # frequency to write tensorboard summaries
  target_network_update_freq: 10}   # frequency to clobber target network params

episode / event hacking for replay

( when --episode-log-file is not specified to we can extract it from recorded stdout. )

$ cat runs/foo.stdout | ./

extract event jsonl from a log (one event per line)

$ cat runs/foo.log | ./ | ./

use previous date to calculate mean/std of ranges

$ cat runs/foo.log | ./ | ./
(68.56658770018004, 30.238156000781927)

rewrite the states for a run. every simualtion involves mapping from ranges -> states that are specific for the policy being trained. by using ./ we can rewrite the ranges (which are always the same) to be a different state sequence. this can be used to build cirruculum style training data from one policy (say a discrete q table) to be used by another policy (say an nn q table).

# rewrite an sequence of episodes so that events retain history of 10 states.
cat runs/foo.log \
 | ./ \
 | ./ 10 \
 > runs/foo_episodes_with_new_states.episode.jsonl

this data can then be piped through ./ and shuffled to build batch experience replay training data.

e.g. replay a sequence of events (one per line) to the /drivebot/training_egs ros topic

# replay events in random order
cat runs/foo.episode.jsonl | ./ | shuf | ./ --rate 1000
# rewrite episodes to have a history length of 5 then replay events in random order ad infinitum
cat runs/foo.episode.jsonl | ./ | ./ > events.jsonl
while true; do shuf events.jsonl | ./ --rate=1000; done


cat runs/foo.episode.jsonl | ./ > episode_id.num_events.total_reward.tsv