yarlp 0.0.9

yarlp

[](https://travis-ci.org/btaba/yarlp)

## yarlp

**Yet Another Reinforcement Learning Package**

Implementations of [`CEM`](/yarlp/agent/cem_agent.py), [`REINFORCE`](/yarlp/agent/pg_agents.py), [`TRPO`](/yarlp/agent/trpo_agent.py), [`DDQN`](/yarlp/agent/ddqn_agent.py), [`A2C`](/yarlp/agent/a2c_agent.py) with reproducible benchmarks. Experiments are templated using `jsonschema` and are compared to published results. This is meant to be a starting point for working implementations of classic RL algorithms. Unfortunately even implementations from OpenAI baselines are [not always reproducible](https://github.com/openai/baselines/issues/176).

A working Dockerfile with `yarlp` installed can be run with:

* `docker build -t "yarlpd" .`
* `docker run -it yarlpd bash`

To run a benchmark, simply:

`python yarlp/experiment/experiment.py --help`


If you want to run things manually, look in `examples` or look at this:

```python
from yarlp.agent.trpo_agent import TRPOAgent
from yarlp.utils.env_utils import NormalizedGymEnv

env = NormalizedGymEnv('MountainCarContinuous-v0')
agent = TRPOAgent(env, seed=123)
agent.train(max_timesteps=1000000)
```

## Benchmarks

We benchmark against published results and Openai [`baselines`](https://github.com/openai/baselines) where available using [`yarlp/experiment/experiment.py`](/yarlp/experiment/experiment.py). Benchmark scripts for Openai `baselines` were made ad-hoc, such as [this one](https://github.com/btaba/baselines/blob/master/baselines/trpo_mpi/run_trpo_experiment.py).

### Atari10M

||||
|---|---|---|
||||
||||


#### DDQN with dueling networks and prioritized replay

`python yarlp/experiment/experiment.py run_atari10m_ddqn_benchmark`


I trained 6 Atari environments for 10M time-steps (**40M frames**), using 1 random seed, since I only have 1 GPU and limited time on this Earth. I used DDQN with dueling networks, but no prioritized replay (although it's implemented). I compare the final mean 100 episode raw scores for yarlp (with exploration of 0.01) with results from [Hasselt et al, 2015](https://arxiv.org/pdf/1509.06461.pdf) and [Wang et al, 2016](https://arxiv.org/pdf/1511.06581.pdf) which train for **200M frames** and evaluate on 100 episodes (exploration of 0.05).

I don't compare to OpenAI baselines because the OpenAI DDQN implementation is **not** currently able to reproduce published results as of 2018-01-20. See [this github issue](https://github.com/openai/baselines/issues/176), although I found [these benchmark plots](https://github.com/openai/baselines-results/blob/master/dqn_results.ipynb) to be pretty helpful.

|env|yarlp DUEL 40M Frames|Hasselt et al DDQN 200M Frames|Wang et al DUEL 200M Frames|
|---|---|---|---|
|BeamRider|8705|7654|12164|
|Breakout|423.5|375|345|
|Pong|20.73|21|21|
|QBert|5410.75|14875|19220.3|
|Seaquest|5300.5|7995|50245.2|
|SpaceInvaders|1978.2|3154.6|6427.3|


| | | | |
|---|---|---|---|
|||||
||||


#### A2C

`python yarlp/experiment/experiment.py run_atari10m_a2c_benchmark`


A2C on 10M time-steps (**40M frames**) with 1 random seed. Results compared to learning curves from [Mnih et al, 2016](https://arxiv.org/pdf/1602.01783.pdf) extracted at 10M time-steps from Figure 3. You are invited to run for multiple seeds and the full 200M frames for a better comparison.

|env|yarlp A2C 40M|Mnih et al A3C 40M 16-threads|
|---|---|---|
|BeamRider|3150|~3000|
|Breakout|418|~150|
|Pong|20|~20|
|QBert|3644|~1000|
|SpaceInvaders|805|~600|

| | | | |
|---|---|---|---|
|||||
||||

Here are some [more plots](https://github.com/openai/baselines-results/blob/master/acktr_ppo_acer_a2c_atari.ipynb) from OpenAI to compare against.

### Mujoco1M

#### TRPO

`python yarlp/experiment/experiment.py run_mujoco1m_benchmark`

We average over 5 random seeds instead of 3 for both `baselines` and `yarlp`. More seeds probably wouldn't hurt here, we report 95th percent confidence intervals.

| | | | |
|---|---|---|---|
|||||
||||


## CLI scripts

CLI convenience scripts will be installed with the package:

* Run a benchmark:
* `python yarlp/experiment/experiment.py --help`
* Plot `yarlp` compared to Openai `baselines` benchmarks:
* `compare_benchmark <yarlp-experiment-dir> <baseline-experiment-dir>`
* Experiments:
* Experiments can be defined using json, validated with `jsonschema`. See [here](/experiment_configs) for sample experiment configs. You can do a grid search if multiple parameters are specified, which will run in parallel.
* Example: `run_yarlp_experiment --spec-file experiment_configs/trpo_experiment_mult_params.json`
* Experiment plots:
* `make_plots <experiment-dir>`