REVIEW 5 cited by
Deep Reinforcement Learning for Autonomous Driving
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. Moreover, the autonomous driving vehicles must also keep functional safety under the complex environments. To deal with these challenges, we first adopt the deep deterministic policy gradient (DDPG) algorithm, which has the capacity to handle complex state and action spaces in continuous domain. We then choose The Open Racing Car Simulator (TORCS) as our environment to avoid physical damage. Meanwhile, we select a set of appropriate sensor information from TORCS and design our own rewarder. In order to fit DDPG algorithm to TORCS, we design our network architecture for both actor and critic inside DDPG paradigm. To demonstrate the effectiveness of our model, We evaluate on different modes in TORCS and show both quantitative and qualitative results.
Forward citations
Cited by 5 Pith papers
-
Solving Markov Decision Processes with Future Information via MPC
Parameterized MPC exactly represents optimal policies for MDPs with future information under identified structural conditions and parameters can be learned via RL.
-
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
CDAN framework uses diversity exploration and adversarial self-correction for continual RL in continuous control, evaluated on new CAM environment with NSD metric showing 18.35% NSD improvement over baseline.
-
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Causal-guided PPO training produces policies that cooperate with rule-based recovery, yielding significant gains in reward, distance, and velocity over non-causal baselines in CARLA driving scenarios.
-
Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline
On a new Waymo-based causal benchmark, an imitation-plus-reinforcement planner (MTR-SAC) reaches goals more often than imitation alone but crashes and goes offroad more, contradicting the claimed safety improvement.
-
Generative AI for Autonomous Driving: A Review
A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.
Discussion (0). Continue with ORCID to comment.