0896f43097
* Add Robosuite parameters for all env types + initialize env flow * Init flow done * Rest of Environment API complete for RobosuiteEnvironment * RobosuiteEnvironment changes * Observation stacking filter * Add proper frame_skip in addition to control_freq * Hardcode Coach rendering to 'frontview' camera * Robosuite_Lift_DDPG preset + Robosuite env updates * Move observation stacking filter from env to preset * Pre-process observation - concatenate depth map (if exists) to image and object state (if exists) to robot state * Preset parameters based on Surreal DDPG parameters, taken from: https://github.com/SurrealAI/surreal/blob/master/surreal/main/ddpg_configs.py * RobosuiteEnvironment fixes - working now with PyGame rendering * Preset minor modifications * ObservationStackingFilter - option to concat non-vector observations * Consider frame skip when setting horizon in robosuite env * Robosuite lift preset - update heatup length and training interval * Robosuite env - change control_freq to 10 to match Surreal usage * Robosuite clipped PPO preset * Distribute multiple workers (-n #) over multiple GPUs * Clipped PPO memory optimization from @shadiendrawis * Fixes to evaluation only workers * RoboSuite_ClippedPPO: Update training interval * Undo last commit (update training interval) * Fix "doube-negative" if conditions * multi-agent single-trainer clipped ppo training with cartpole * cleanups (not done yet) + ~tuned hyper-params for mast * Switch to Robosuite v1 APIs * Change presets to IK controller * more cleanups + enabling evaluation worker + better logging * RoboSuite_Lift_ClippedPPO updates * Fix major bug in obs normalization filter setup * Reduce coupling between Robosuite API and Coach environment * Now only non task-specific parameters are explicitly defined in Coach * Removed a bunch of enums of Robosuite elements, using simple strings instead * With this change new environments/robots/controllers in Robosuite can be used immediately in Coach * MAST: better logging of actor-trainer interaction + bug fixes + performance improvements. Still missing: fixed pubsub for obs normalization running stats + logging for trainer signals * lstm support for ppo * setting JOINT VELOCITY action space by default + fix for EveryNEpisodes video dump filter + new TaskIDDumpFilter + allowing or between video dump filters * Separate Robosuite clipped PPO preset for the non-MAST case * Add flatten layer to architectures and use it in Robosuite presets This is required for embedders that mix conv and dense TODO: Add MXNet implementation * publishing running_stats together with the published policy + hyper-param for when to publish a policy + cleanups * bug-fix for memory leak in MAST * Bugfix: Return value in TF BatchnormActivationDropout.to_tf_instance * Explicit activations in embedder scheme so there's no ReLU after flatten * Add clipped PPO heads with configurable dense layers at the beginning * This is a workaround needed to mimic Surreal-PPO, where the CNN and LSTM are shared between actor and critic but the FC layers are not shared * Added a "SchemeBuilder" class, currently only used for the new heads but we can change Middleware and Embedder implementations to use it as well * Video dump setting fix in basic preset * logging screen output to file * coach to start the redis-server for a MAST run * trainer drops off-policy data + old policy in ClippedPPO updates only after policy was published + logging free memory stats + actors check for a new policy only at the beginning of a new episode + fixed a bug where the trainer was logging "Training Reward = 0", causing dashboard to incorrectly display the signal * Add missing set_internal_state function in TFSharedRunningStats * Robosuite preset - use SingleLevelSelect instead of hard-coded level * policy ID published directly on Redis * Small fix when writing to log file * Major bugfix in Robosuite presets - pass dense sizes to heads * RoboSuite_Lift_ClippedPPO hyper-params update * add horizon and value bootstrap to GAE calculation, fix A3C with LSTM * adam hyper-params from mujoco * updated MAST preset with IK_POSE_POS controller * configurable initialization for policy stdev + custom extra noise per actor + logging of policy stdev to dashboard * values loss weighting of 0.5 * minor fixes + presets * bug-fix for MAST where the old policy in the trainer had kept updating every training iter while it should only update after every policy publish * bug-fix: reset_internal_state was not called by the trainer * bug-fixes in the lstm flow + some hyper-param adjustments for CartPole_ClippedPPO_LSTM -> training and sometimes reaches 200 * adding back the horizon hyper-param - a messy commit * another bug-fix missing from prev commit * set control_freq=2 to match action_scale 0.125 * ClippedPPO with MAST cleanups and some preps for TD3 with MAST * TD3 presets. RoboSuite_Lift_TD3 seems to work well with multi-process runs (-n 8) * setting termination on collision to be on by default * bug-fix following prev-prev commit * initial cube exploration environment with TD3 commit * bug fix + minor refactoring * several parameter changes and RND debugging * Robosuite Gym wrapper + Rename TD3_Random* -> Random* * algorithm update * Add RoboSuite v1 env + presets (to eventually replace non-v1 ones) * Remove grasping presets, keep only V1 exp. presets (w/o V1 tag) * Keep just robosuite V1 env as the 'robosuite_environment' module * Exclude Robosuite and MAST presets from integration tests * Exclude LSTM and MAST presets from golden tests * Fix mistakenly removed import * Revert debug changes in ReaderWriterLock * Try another way to exclude LSTM/MAST golden tests * Remove debug prints * Remove PreDense heads, unused in the end * Missed removing an instance of PreDense head * Remove MAST, not required for this PR * Undo unused concat option in ObservationStackingFilter * Remove LSTM updates, not required in this PR * Update README.md * code changes for the exploration flow to work with robosuite master branch * code cleanup + documentation * jupyter tutorial for the goal-based exploration + scatter plot * typo fix * Update README.md * seprate parameter for the obs-goal observation + small fixes * code clarity fixes * adjustment in tutorial 5 * Update tutorial * Update tutorial Co-authored-by: Guy Jacob <guy.jacob@intel.com> Co-authored-by: Gal Leibovich <gal.leibovich@intel.com> Co-authored-by: shadi.endrawis <sendrawi@aipg-ra-skx-03.ra.intel.com>
Coach - Tests
Coach is a complex framework consisting of various features and running schemes. On top of that, reinforcement learning adds stochasticity in many places along the experiments, which makes getting the same results run-after-run is almost impossible. To address those issues, and ensure that Coach keeps working as expected, we separated our testing mechanism into several parts, each testing the framework in different areas and strictness.
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Docker -
The docker image we supply checks Coach in terms of installation process, and verifies that all the components are installed correctly. To build the Docker image, use the command:
cd docker make build_base && make build make run -
Unit tests -
The unit tests test sub components of Coach with different parameters and verifies that they work as expected. There are currently tens of tests and we keep adding new ones. We use pytest in order to run the tests, using the following command:
python3 -m pytest rl_coach/tests -m unit_test -
Integration tests -
The integration tests make sure that all the presets are runnable. It's a static tests that does not check the performance at all. It only checks that the preset can start running with no import error or other bugs. To run the integration tests, use the following command:
python3 -m pytest rl_coach/tests -m integration_test -
Golden tests -
The golden tests run a subset of the presets available in Coach, and verify that they pass a known score after a known amount of steps. The threshold for the tests are defined as part of each preset. The presets which are tested are presets that can be run in a short amount of time, and the requirements for passing are quite weak. The golden tests can be run using the following command:
python3 -m pytest rl_coach/tests -m golden_test -
Trace tests -
The trace tests run all the presets available in Coach, and compare their csv output to traces we extracted after verifying each preset works correctly. The requirements for passing these tests are quite strict - all the values in the csv file should match the golden csv file exactly. The trace tests can be run in parallel to shorten the testing time. To run the tests in parallel use the following command:
python3 rl_coach/tests/trace_tests.py -prl -
Optional PyTest Flags -
Using -k expr to select tests based on their name; The -k command line option to specify an expression which implements a substring match on the test names instead of the exact match on markers that -m provides. This makes it easy to select tests based on their names:
python3 -m pytest rl_coach/tests -k DoomUsing -v (--verbose) expr to show tests progress during running the tests, -v can be added with -m or with -k, to use -v see the following commands:
python3 -m pytest rl_coach/tests -v -m golden_test OR python3 -m pytest rl_coach/tests -v -k Doom