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Twin Delayed Deep Deterministic Policy Gradient (TD3)

Overview

TD3 is a popular DRL algorithm for continuous control. It extends DDPG with three techniques: 1) Clipped Double Q-Learning, 2) Delayed Policy Updates, and 3) Target Policy Smoothing Regularization. With these three techniques TD3 shows significantly better performance compared to DDPG.

Original paper:

Reference resources:

Implemented Variants

Variants Implemented Description
td3_continuous_action.py, docs For continuous action space

Below are our single-file implementations of TD3:

td3_continuous_action.py

The td3_continuous_action.py has the following features:

  • For continuous action space
  • Works with the Box observation space of low-level features
  • Works with the Box (continuous) action space

Usage

poetry install
poetry install -E pybullet
python cleanrl/td3_continuous_action.py --help
python cleanrl/td3_continuous_action.py --env-id HopperBulletEnv-v0
poetry install -E mujoco # only works in Linux
python cleanrl/td3_continuous_action.py --env-id Hopper-v3

Explanation of the logged metrics

Running python cleanrl/td3_continuous_action.py will automatically record various metrics such as various losses in Tensorboard. Below are the documentation for these metrics:

  • charts/episodic_return: episodic return of the game
  • charts/SPS: number of steps per second
  • losses/qf1_loss: the MSE between the Q values at timestep \(t\) and the target Q values at timestep \(t+1\), which minimizes temporal difference.
  • losses/actor_loss: implemented as -qf1(data.observations, actor(data.observations)).mean(); it is the negative average Q values calculated based on the 1) observations and the 2) actions computed by the actor based on these observations. By minimizing actor_loss, the optimizer updates the actors parameter using the following gradient (Fujimoto et al., 2018, Algorithm 1)2:
\[ \nabla_{\phi} J(\phi)=\left.N^{-1} \sum \nabla_{a} Q_{\theta_{1}}(s, a)\right|_{a=\pi_{\phi}(s)} \nabla_{\phi} \pi_{\phi}(s) \]
  • losses/qf1_values: implemented as `qf1(data.observations, data.actions).view(-1); it is the average Q values of the sampled data in the replay buffer; useful when gauging if under or over esitmations happen

Implementation details

Our td3_continuous_action.py is based on the TD3.py from sfujim/TD3. Our td3_continuous_action.py presents the following implementation differences.

  1. td3_continuous_action.py uses a two separate objects qf1 and qf2 to represents the two Q functions in the Clipped Double Q-learning architecture, whereas TD3.py (Fujimoto et al., 2018)2 uses a single Critic class that contains both Q networks. That said, these two implementations are virtually the same.

Experiment results

To run benchmark experiments, see benchmark/td3.sh. Specifically, execute the following command:

Below are the average episodic returns for td3_continuous_action.py (3 random seeds). To ensure the quality of the implementation, we compared the results against (Fujimoto et al., 2018)2.

Environment td3_continuous_action.py TD3.py (Fujimoto et al., 2018, Table 1)2
HalfCheetah 9018.31 ± 1078.31 9636.95 ± 859.065
Walker2d 4246.07 ± 1210.84 4682.82 ± 539.64
Hopper 3391.78 ± 232.21 3564.07 ± 114.74
Info

Note that td3_continuous_action.py uses gym MuJoCo v2 environments while TD3.py (Fujimoto et al., 2018)2 uses the gym MuJoCo v1 environments. According to the openai/gym#834, gym MuJoCo v2 environments should be equivalent to the gym MuJoCo v1 environments.

Also note the performance of our td3_continuous_action.py seems to be worse than the reference implementation on Walker2d. This is likely due to openai/gym#938. We would have a hard time reproducing gym MuJoCo v1 environments because they have been long deprecated.

One other thing could cause the performance difference: the original code reported the average episodic return using determinisitc evaluation (i.e., without exploration noise), see sfujim/TD3/main.py#L15-L32, whereas we reported the episodic return during training and the policy gets updated between environments steps.

Learning curves:

Tracked experiments and game play videos:

  1. Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N.M., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2016). Continuous control with deep reinforcement learning. CoRR, abs/1509.02971. https://arxiv.org/abs/1509.02971 ↩

  2. Fujimoto, S., Hoof, H.V., & Meger, D. (2018). Addressing Function Approximation Error in Actor-Critic Methods. ArXiv, abs/1802.09477. https://arxiv.org/abs/1802.09477 ↩↩↩↩↩

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