Benchmark Performance
Reinforcement learning algorithms are difficult to debug and test.
For this reason, in order to ensuring the correctness of the preset agents provided by the autonomous-learning-library
,
we benchmark each algorithm after every major change.
We also discuss the performance of our implementations relative to published results.
For our hyperparameters for each domain, see all.presets.
Atari Benchmark
To benchmark the all.presets.atari
presets, we ran each agent for 10 million timesteps (40 million in-game frames).
The learning rate was decayed over the course of training using cosine annealing.
The environment implementation uses the following wrappers:
NoopResetEnv (adds a random number of noops at the beginning of each game reset)
MaxAndSkipEnv (Repeats each action four times before the next agent observation. Takes the max pixel value over the four frames.)
FireResetEnv (Automatically chooses the “FIRE” action when env.reset() is called)
WarpFrame (Rescales the frame to 84x84 and greyscales the image)
LifeLostEnv (Adds a key to “info” indicating that a life was lost)
Additionally, we use the following agent “bodies”:
FrameStack (provides the last four frames as the state)
ClipRewards (Converts all rewards to {-1, 0, 1})
EpisodicLives (If life was lost, treats the frame as the end of an episode)
The results were as follows:
For comparison, we look at the results published in the paper, Rainbow: Combining Improvements in Deep Reinforcement Learning:
In these results, the authors ran each agent for 50 million timesteps (200 million frames).
We can see that at the 10 million timestep mark, our results are similar or slightly better.
Our dqn
and ddqn
in particular were better almost across the board.
While there are some minor implementation differences (for example, we use Adam
for most algorithms instead of RMSprop
),
our agents achieved very similar behavior to the agents tested by DeepMind.
MuJoCo Benchmark
MuJoCo is “a free and open source physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed.” The MuJoCo Gym environments are a common benchmark in RL research for evaluating agents with continuous action spaces. We ran each continuous preset for 5 million timesteps (in this case, timesteps are equal to frames). The learning rate was decayed over the course of training using cosine annealing. The results were as follows:
These results are similar to results found elsewhere, and in some cases better. However, results can very based on hyperparameter tuning, implementation specifics, and the random seed.
PyBullet Benchmark
PyBullet provides a free alternative to the popular MuJoCo robotics environments. We ran each agent for 5 million timesteps (in this case, timesteps are equal to frames). The learning rate was decayed over the course of training using cosine annealing. The results were as follows:
Because most research papers still use MuJoCo, direct comparisons are difficult to come by. However, George Sung helpfully benchmarked TD3 and DDPG on several PyBullet environments [here](https://github.com/georgesung/TD3). However, he only ran each environment for 1 million timesteps and tuned his hyperparameters accordingly. Generally, our agents achieved higher final perfomance but converged more slowly.